HNSCC UNM Althoff et al_Clinical Analysis
library(swimplot) library(grid) library(gtable) library(readr) library(mosaic) library(forcats) library(dplyr) library(survival) library(survminer) library(ggplot2) library(scales) library(coxphf) library(ggthemes) library(tidyverse) library(gtsummary) library(flextable) library(reshape2) library(parameters) library(car) library(ComplexHeatmap) library(tidyverse) library(readxl) library(janitor) library(DT) library(pROC) library(rms)
#ctDNA Detection Rates by Window and Stages
#ctDNA at Baseline
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.Base %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Base == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Base, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Base == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data) Stage Total_Count Positive_Count Rate
1 I/II 34 32 94.12%
2 III/IVA/IVB 27 22 81.48%
3 IVC 2 2 100.00%
4 Overall 63 56 88.89%#ctDNA at MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data) Stage Total_Count Positive_Count Rate
1 I/II 34 5 14.71%
2 III/IVA/IVB 33 7 21.21%
3 IVC 2 1 50.00%
4 Overall 69 13 18.84%#ctDNA at Surveillance
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data) Stage Total_Count Positive_Count Rate
1 I/II 42 10 23.81%
2 III/IVA/IVB 35 13 37.14%
3 IVC 2 1 50.00%
4 Overall 79 24 30.38%#Demographics Table
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data_subset <- circ_data %>%
select(
Sex,
Age,
Tobacco.History,
Prim.Location,
cT,
cN,
cM,
Histology,
Stage,
p16.status,
Treatment.Group,
PFS.Event,
OS.Event,
OS.months) %>%
mutate(
Sex = factor(Sex),
Age = as.numeric(Age),
Tobacco.History = factor(Tobacco.History),
Prim.Location = factor(Prim.Location),
cT = factor(cT),
cN = factor(cN),
cM = factor(cM),
Histology = factor(Histology),
Stage = factor(Stage),
p16.status = factor(p16.status),
Treatment.Group = factor(Treatment.Group),
PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
OS.months = as.numeric(OS.months))
table1 <- circ_data_subset %>%
tbl_summary(
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
bold_labels()
table1| Characteristic | N = 971 |
|---|---|
| Sex | |
| Female | 17 (18%) |
| Male | 80 (82%) |
| Age | 67 (30 - 96) |
| Tobacco.History | 63 (65%) |
| Prim.Location | |
| Larynx/Hypopharynx | 5 (5.2%) |
| Oral cavity | 16 (16%) |
| Oropharynx | 67 (69%) |
| Other (paranasal sinus and nasopharyngeal) | 9 (9.3%) |
| cT | |
| T0 | 2 (2.1%) |
| T1 | 12 (12%) |
| T2 | 31 (32%) |
| T3 | 30 (31%) |
| T4 | 21 (22%) |
| TX | 1 (1.0%) |
| cN | |
| N0 | 22 (23%) |
| N1 | 33 (34%) |
| N2 | 33 (34%) |
| N3 | 9 (9.3%) |
| cM | |
| M0 | 93 (96%) |
| M1 | 4 (4.1%) |
| Histology | |
| Adenosquamous carcinoma | 1 (1.0%) |
| Basaloid squamous cell carcinoma | 6 (6.2%) |
| Epithelial myoepithelial carcinoma | 1 (1.0%) |
| Squamous cell carcinoma | 86 (89%) |
| Undifferentiated carcinoma | 3 (3.1%) |
| Stage | |
| I/II | 49 (51%) |
| III/IVA/IVB | 45 (46%) |
| IVC | 3 (3.1%) |
| p16.status | |
| Negative | 43 (44%) |
| Positive | 54 (56%) |
| Treatment.Group | |
| Definitive CRT or RT | 69 (71%) |
| None (Declined Treatment) | 1 (1.0%) |
| None (Hospice) | 2 (2.1%) |
| Surgery + CRT or RT | 24 (25%) |
| Surgery only | 1 (1.0%) |
| PFS.Event | |
| No Progression | 63 (65%) |
| Progression | 34 (35%) |
| OS.Event | |
| Alive | 81 (84%) |
| Deceased | 16 (16%) |
| OS.months | 22 (2 - 56) |
| 1 n (%); Median (Min - Max) | |
fit1 <- as_flex_table(
table1,
include = everything(),
return_calls = FALSE
)
fit1Characteristic | N = 971 |
|---|---|
Sex | |
Female | 17 (18%) |
Male | 80 (82%) |
Age | 67 (30 - 96) |
Tobacco.History | 63 (65%) |
Prim.Location | |
Larynx/Hypopharynx | 5 (5.2%) |
Oral cavity | 16 (16%) |
Oropharynx | 67 (69%) |
Other (paranasal sinus and nasopharyngeal) | 9 (9.3%) |
cT | |
T0 | 2 (2.1%) |
T1 | 12 (12%) |
T2 | 31 (32%) |
T3 | 30 (31%) |
T4 | 21 (22%) |
TX | 1 (1.0%) |
cN | |
N0 | 22 (23%) |
N1 | 33 (34%) |
N2 | 33 (34%) |
N3 | 9 (9.3%) |
cM | |
M0 | 93 (96%) |
M1 | 4 (4.1%) |
Histology | |
Adenosquamous carcinoma | 1 (1.0%) |
Basaloid squamous cell carcinoma | 6 (6.2%) |
Epithelial myoepithelial carcinoma | 1 (1.0%) |
Squamous cell carcinoma | 86 (89%) |
Undifferentiated carcinoma | 3 (3.1%) |
Stage | |
I/II | 49 (51%) |
III/IVA/IVB | 45 (46%) |
IVC | 3 (3.1%) |
p16.status | |
Negative | 43 (44%) |
Positive | 54 (56%) |
Treatment.Group | |
Definitive CRT or RT | 69 (71%) |
None (Declined Treatment) | 1 (1.0%) |
None (Hospice) | 2 (2.1%) |
Surgery + CRT or RT | 24 (25%) |
Surgery only | 1 (1.0%) |
PFS.Event | |
No Progression | 63 (65%) |
Progression | 34 (35%) |
OS.Event | |
Alive | 81 (84%) |
Deceased | 16 (16%) |
OS.months | 22 (2 - 56) |
1n (%); Median (Min - Max) | |
save_as_docx(fit1, path= "~/Downloads/1. CLIA HNSCC UNM Demographics Table.docx")#Demographics Table by ctDNA at baseline
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data_subset1 <- circ_data %>%
select(
Sex,
Age,
Tobacco.History,
Prim.Location,
cT,
cN,
cM,
Histology,
Stage,
p16.status,
Treatment.Group,
PFS.Event,
OS.Event,
OS.months) %>%
mutate(
Sex = factor(Sex),
Age = as.numeric(Age),
Tobacco.History = factor(Tobacco.History),
Prim.Location = factor(Prim.Location),
cT = factor(cT),
cN = factor(cN),
cM = factor(cM),
Histology = factor(Histology),
Stage = factor(Stage),
p16.status = factor(p16.status),
Treatment.Group = factor(Treatment.Group),
PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
OS.months = as.numeric(OS.months))
circ_data1 <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data_subset2 <- circ_data1 %>%
select(
Sex,
Age,
Tobacco.History,
Prim.Location,
cT,
cN,
cM,
Histology,
Stage,
p16.status,
Treatment.Group,
PFS.Event,
OS.Event,
OS.months,
ctDNA.Base) %>%
mutate(
Sex = factor(Sex),
Age = as.numeric(Age),
Tobacco.History = factor(Tobacco.History),
Prim.Location = factor(Prim.Location),
cT = factor(cT),
cN = factor(cN),
cM = factor(cM),
Histology = factor(Histology),
Stage = factor(Stage),
p16.status = factor(p16.status),
Treatment.Group = factor(Treatment.Group),
PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
OS.months = as.numeric(OS.months),
ctDNA.Base = factor(ctDNA.Base, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive")))
Overall <- circ_data_subset1 %>%
tbl_summary(
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
bold_labels()
Overall| Characteristic | N = 971 |
|---|---|
| Sex | |
| Female | 17 (18%) |
| Male | 80 (82%) |
| Age | 67 (30 - 96) |
| Tobacco.History | 63 (65%) |
| Prim.Location | |
| Larynx/Hypopharynx | 5 (5.2%) |
| Oral cavity | 16 (16%) |
| Oropharynx | 67 (69%) |
| Other (paranasal sinus and nasopharyngeal) | 9 (9.3%) |
| cT | |
| T0 | 2 (2.1%) |
| T1 | 12 (12%) |
| T2 | 31 (32%) |
| T3 | 30 (31%) |
| T4 | 21 (22%) |
| TX | 1 (1.0%) |
| cN | |
| N0 | 22 (23%) |
| N1 | 33 (34%) |
| N2 | 33 (34%) |
| N3 | 9 (9.3%) |
| cM | |
| M0 | 93 (96%) |
| M1 | 4 (4.1%) |
| Histology | |
| Adenosquamous carcinoma | 1 (1.0%) |
| Basaloid squamous cell carcinoma | 6 (6.2%) |
| Epithelial myoepithelial carcinoma | 1 (1.0%) |
| Squamous cell carcinoma | 86 (89%) |
| Undifferentiated carcinoma | 3 (3.1%) |
| Stage | |
| I/II | 49 (51%) |
| III/IVA/IVB | 45 (46%) |
| IVC | 3 (3.1%) |
| p16.status | |
| Negative | 43 (44%) |
| Positive | 54 (56%) |
| Treatment.Group | |
| Definitive CRT or RT | 69 (71%) |
| None (Declined Treatment) | 1 (1.0%) |
| None (Hospice) | 2 (2.1%) |
| Surgery + CRT or RT | 24 (25%) |
| Surgery only | 1 (1.0%) |
| PFS.Event | |
| No Progression | 63 (65%) |
| Progression | 34 (35%) |
| OS.Event | |
| Alive | 81 (84%) |
| Deceased | 16 (16%) |
| OS.months | 22 (2 - 56) |
| 1 n (%); Median (Min - Max) | |
ByctDNA_MRD <- circ_data_subset2 %>%
tbl_summary(
by = ctDNA.Base, # add this line to subgroup by ctDNA.Base
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
add_p() %>%
bold_labels()
ByctDNA_MRD| Characteristic | Negative N = 71 |
Positive N = 561 |
p-value2 |
|---|---|---|---|
| Sex | 0.10 | ||
| Female | 3 (43%) | 8 (14%) | |
| Male | 4 (57%) | 48 (86%) | |
| Age | 81 (54 - 96) | 66 (38 - 96) | 0.080 |
| Tobacco.History | 4 (57%) | 38 (68%) | 0.7 |
| Prim.Location | 0.066 | ||
| Larynx/Hypopharynx | 0 (0%) | 3 (5.4%) | |
| Oral cavity | 3 (43%) | 5 (8.9%) | |
| Oropharynx | 3 (43%) | 44 (79%) | |
| Other (paranasal sinus and nasopharyngeal) | 1 (14%) | 4 (7.1%) | |
| cT | 0.051 | ||
| T0 | 0 (0%) | 2 (3.6%) | |
| T1 | 1 (14%) | 4 (7.1%) | |
| T2 | 2 (29%) | 20 (36%) | |
| T3 | 0 (0%) | 21 (38%) | |
| T4 | 4 (57%) | 9 (16%) | |
| TX | 0 (0%) | 0 (0%) | |
| cN | >0.9 | ||
| N0 | 1 (14%) | 11 (20%) | |
| N1 | 3 (43%) | 19 (34%) | |
| N2 | 3 (43%) | 20 (36%) | |
| N3 | 0 (0%) | 6 (11%) | |
| cM | >0.9 | ||
| M0 | 7 (100%) | 54 (96%) | |
| M1 | 0 (0%) | 2 (3.6%) | |
| Histology | 0.14 | ||
| Adenosquamous carcinoma | 0 (0%) | 0 (0%) | |
| Basaloid squamous cell carcinoma | 0 (0%) | 3 (5.4%) | |
| Epithelial myoepithelial carcinoma | 0 (0%) | 0 (0%) | |
| Squamous cell carcinoma | 6 (86%) | 53 (95%) | |
| Undifferentiated carcinoma | 1 (14%) | 0 (0%) | |
| Stage | 0.4 | ||
| I/II | 2 (29%) | 32 (57%) | |
| III/IVA/IVB | 5 (71%) | 22 (39%) | |
| IVC | 0 (0%) | 2 (3.6%) | |
| p16.status | 0.095 | ||
| Negative | 5 (71%) | 19 (34%) | |
| Positive | 2 (29%) | 37 (66%) | |
| Treatment.Group | 0.3 | ||
| Definitive CRT or RT | 5 (71%) | 51 (91%) | |
| None (Declined Treatment) | 0 (0%) | 0 (0%) | |
| None (Hospice) | 0 (0%) | 1 (1.8%) | |
| Surgery + CRT or RT | 2 (29%) | 3 (5.4%) | |
| Surgery only | 0 (0%) | 1 (1.8%) | |
| PFS.Event | 0.4 | ||
| No Progression | 6 (86%) | 35 (63%) | |
| Progression | 1 (14%) | 21 (38%) | |
| OS.Event | 0.3 | ||
| Alive | 7 (100%) | 44 (79%) | |
| Deceased | 0 (0%) | 12 (21%) | |
| OS.months | 31 (21 - 39) | 16 (2 - 45) | 0.026 |
| 1 n (%); Median (Min - Max) | |||
| 2 Fisher’s exact test; Wilcoxon rank sum test | |||
merged_table <- tbl_merge(tbls=list(Overall, ByctDNA_MRD))
merged_table| Characteristic |
Table 1
|
Table 2
|
||
|---|---|---|---|---|
| N = 971 | Negative N = 71 |
Positive N = 561 |
p-value2 | |
| Sex | 0.10 | |||
| Female | 17 (18%) | 3 (43%) | 8 (14%) | |
| Male | 80 (82%) | 4 (57%) | 48 (86%) | |
| Age | 67 (30 - 96) | 81 (54 - 96) | 66 (38 - 96) | 0.080 |
| Tobacco.History | 63 (65%) | 4 (57%) | 38 (68%) | 0.7 |
| Prim.Location | 0.066 | |||
| Larynx/Hypopharynx | 5 (5.2%) | 0 (0%) | 3 (5.4%) | |
| Oral cavity | 16 (16%) | 3 (43%) | 5 (8.9%) | |
| Oropharynx | 67 (69%) | 3 (43%) | 44 (79%) | |
| Other (paranasal sinus and nasopharyngeal) | 9 (9.3%) | 1 (14%) | 4 (7.1%) | |
| cT | 0.051 | |||
| T0 | 2 (2.1%) | 0 (0%) | 2 (3.6%) | |
| T1 | 12 (12%) | 1 (14%) | 4 (7.1%) | |
| T2 | 31 (32%) | 2 (29%) | 20 (36%) | |
| T3 | 30 (31%) | 0 (0%) | 21 (38%) | |
| T4 | 21 (22%) | 4 (57%) | 9 (16%) | |
| TX | 1 (1.0%) | 0 (0%) | 0 (0%) | |
| cN | >0.9 | |||
| N0 | 22 (23%) | 1 (14%) | 11 (20%) | |
| N1 | 33 (34%) | 3 (43%) | 19 (34%) | |
| N2 | 33 (34%) | 3 (43%) | 20 (36%) | |
| N3 | 9 (9.3%) | 0 (0%) | 6 (11%) | |
| cM | >0.9 | |||
| M0 | 93 (96%) | 7 (100%) | 54 (96%) | |
| M1 | 4 (4.1%) | 0 (0%) | 2 (3.6%) | |
| Histology | 0.14 | |||
| Adenosquamous carcinoma | 1 (1.0%) | 0 (0%) | 0 (0%) | |
| Basaloid squamous cell carcinoma | 6 (6.2%) | 0 (0%) | 3 (5.4%) | |
| Epithelial myoepithelial carcinoma | 1 (1.0%) | 0 (0%) | 0 (0%) | |
| Squamous cell carcinoma | 86 (89%) | 6 (86%) | 53 (95%) | |
| Undifferentiated carcinoma | 3 (3.1%) | 1 (14%) | 0 (0%) | |
| Stage | 0.4 | |||
| I/II | 49 (51%) | 2 (29%) | 32 (57%) | |
| III/IVA/IVB | 45 (46%) | 5 (71%) | 22 (39%) | |
| IVC | 3 (3.1%) | 0 (0%) | 2 (3.6%) | |
| p16.status | 0.095 | |||
| Negative | 43 (44%) | 5 (71%) | 19 (34%) | |
| Positive | 54 (56%) | 2 (29%) | 37 (66%) | |
| Treatment.Group | 0.3 | |||
| Definitive CRT or RT | 69 (71%) | 5 (71%) | 51 (91%) | |
| None (Declined Treatment) | 1 (1.0%) | 0 (0%) | 0 (0%) | |
| None (Hospice) | 2 (2.1%) | 0 (0%) | 1 (1.8%) | |
| Surgery + CRT or RT | 24 (25%) | 2 (29%) | 3 (5.4%) | |
| Surgery only | 1 (1.0%) | 0 (0%) | 1 (1.8%) | |
| PFS.Event | 0.4 | |||
| No Progression | 63 (65%) | 6 (86%) | 35 (63%) | |
| Progression | 34 (35%) | 1 (14%) | 21 (38%) | |
| OS.Event | 0.3 | |||
| Alive | 81 (84%) | 7 (100%) | 44 (79%) | |
| Deceased | 16 (16%) | 0 (0%) | 12 (21%) | |
| OS.months | 22 (2 - 56) | 31 (21 - 39) | 16 (2 - 45) | 0.026 |
| 1 n (%); Median (Min - Max) | ||||
| 2 Fisher’s exact test; Wilcoxon rank sum test | ||||
fit1 <- as_flex_table(
merged_table,
include = everything(),
return_calls = FALSE
)
fit1
| Table 1 | Table 2 | ||
|---|---|---|---|---|
Characteristic | N = 971 | Negative | Positive | p-value2 |
Sex | 0.10 | |||
Female | 17 (18%) | 3 (43%) | 8 (14%) | |
Male | 80 (82%) | 4 (57%) | 48 (86%) | |
Age | 67 (30 - 96) | 81 (54 - 96) | 66 (38 - 96) | 0.080 |
Tobacco.History | 63 (65%) | 4 (57%) | 38 (68%) | 0.7 |
Prim.Location | 0.066 | |||
Larynx/Hypopharynx | 5 (5.2%) | 0 (0%) | 3 (5.4%) | |
Oral cavity | 16 (16%) | 3 (43%) | 5 (8.9%) | |
Oropharynx | 67 (69%) | 3 (43%) | 44 (79%) | |
Other (paranasal sinus and nasopharyngeal) | 9 (9.3%) | 1 (14%) | 4 (7.1%) | |
cT | 0.051 | |||
T0 | 2 (2.1%) | 0 (0%) | 2 (3.6%) | |
T1 | 12 (12%) | 1 (14%) | 4 (7.1%) | |
T2 | 31 (32%) | 2 (29%) | 20 (36%) | |
T3 | 30 (31%) | 0 (0%) | 21 (38%) | |
T4 | 21 (22%) | 4 (57%) | 9 (16%) | |
TX | 1 (1.0%) | 0 (0%) | 0 (0%) | |
cN | >0.9 | |||
N0 | 22 (23%) | 1 (14%) | 11 (20%) | |
N1 | 33 (34%) | 3 (43%) | 19 (34%) | |
N2 | 33 (34%) | 3 (43%) | 20 (36%) | |
N3 | 9 (9.3%) | 0 (0%) | 6 (11%) | |
cM | >0.9 | |||
M0 | 93 (96%) | 7 (100%) | 54 (96%) | |
M1 | 4 (4.1%) | 0 (0%) | 2 (3.6%) | |
Histology | 0.14 | |||
Adenosquamous carcinoma | 1 (1.0%) | 0 (0%) | 0 (0%) | |
Basaloid squamous cell carcinoma | 6 (6.2%) | 0 (0%) | 3 (5.4%) | |
Epithelial myoepithelial carcinoma | 1 (1.0%) | 0 (0%) | 0 (0%) | |
Squamous cell carcinoma | 86 (89%) | 6 (86%) | 53 (95%) | |
Undifferentiated carcinoma | 3 (3.1%) | 1 (14%) | 0 (0%) | |
Stage | 0.4 | |||
I/II | 49 (51%) | 2 (29%) | 32 (57%) | |
III/IVA/IVB | 45 (46%) | 5 (71%) | 22 (39%) | |
IVC | 3 (3.1%) | 0 (0%) | 2 (3.6%) | |
p16.status | 0.095 | |||
Negative | 43 (44%) | 5 (71%) | 19 (34%) | |
Positive | 54 (56%) | 2 (29%) | 37 (66%) | |
Treatment.Group | 0.3 | |||
Definitive CRT or RT | 69 (71%) | 5 (71%) | 51 (91%) | |
None (Declined Treatment) | 1 (1.0%) | 0 (0%) | 0 (0%) | |
None (Hospice) | 2 (2.1%) | 0 (0%) | 1 (1.8%) | |
Surgery + CRT or RT | 24 (25%) | 2 (29%) | 3 (5.4%) | |
Surgery only | 1 (1.0%) | 0 (0%) | 1 (1.8%) | |
PFS.Event | 0.4 | |||
No Progression | 63 (65%) | 6 (86%) | 35 (63%) | |
Progression | 34 (35%) | 1 (14%) | 21 (38%) | |
OS.Event | 0.3 | |||
Alive | 81 (84%) | 7 (100%) | 44 (79%) | |
Deceased | 16 (16%) | 0 (0%) | 12 (21%) | |
OS.months | 22 (2 - 56) | 31 (21 - 39) | 16 (2 - 45) | 0.026 |
1n (%); Median (Min - Max) | ||||
2Fisher's exact test; Wilcoxon rank sum test | ||||
save_as_docx(fit1, path = "~/Downloads/1b. CLIA HNSCC UNM Demographics Table by ctDNA.docx")#Overview plot by Stage
setwd("~/Downloads")
clinstage <- read.csv("CLIA HNSCC UNM_OP.csv")
clinstage_df <- as.data.frame(clinstage)
# Creating the basic swimmer plot
oplot <- swimmer_plot(df=clinstage_df,
id='PatientName',
end='fu.diff.months',
fill='gray',
width=.01,
base_size = 14,
stratify= c('Stage'))
# Adding themes and scales
oplot <- oplot + theme(panel.border = element_blank())
oplot <- oplot + scale_y_continuous(breaks = seq(0, 72, by = 3))
oplot <- oplot + labs(x ="Patients", y="Months from Diagnosis")
# Adding swimmer points
oplot_ev1 <- oplot + swimmer_points(df_points=clinstage_df,
id='PatientName',
time='date.diff.months',
name_shape ='Event_type',
name_col = 'Event',
size=3.5,fill='black')
# Optionally uncomment and use col='darkgreen' if needed
# Adding shape manual scale
oplot_ev1.1 <- oplot_ev1 + ggplot2::scale_shape_manual(name="Event_type",
values=c(1,16,6,18,18,4),
breaks=c('ctDNA_neg','ctDNA_pos', 'Imaging','Surgery','Biopsy', 'Death'))
# Display the plot
oplot_ev1.1
oplot_ev2 <- oplot_ev1.1 + swimmer_lines(df_lines=clinstage_df,
id='PatientName',
start='Tx_start.months',
end='Tx_end.months',
name_col='Tx_type',
size=3.5,
name_alpha = 1.0)
oplot_ev2 <- oplot_ev2 + guides(linetype = guide_legend(override.aes = list(size = 5, color = "black")))
oplot_ev2
oplot_ev2.2 <- oplot_ev2 + ggplot2::scale_color_manual(name="Event",values=c( "grey", "orange", "black", "black", "green", "red", "purple", "blue"))
oplot_ev2.2
#PFS in Complete Cohort (N=97)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.available, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.available, data = circ_data)
n events median 0.95LCL 0.95UCL
[1,] 97 34 40.1 40.1 NAsurv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.available, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue"), title="PFS - Complete Cohort (n=97)", ylab= "Progression-Free Survival", xlab="Months from Start of definitive Treatment", legend.labs=c("Complete cohort"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.available, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 64 23 0.760 0.0438 0.660 0.833
24 36 8 0.651 0.0520 0.538 0.742
36 13 2 0.612 0.0555 0.494 0.711#OS in Complete Cohort (N=97)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.available, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.available, data = circ_data)
n events median 0.95LCL 0.95UCL
[1,] 97 16 NA NA NAsurv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.available, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue"), title="OS - Complete Cohort (n=97)", ylab= "Overall Survival", xlab="Months from Start of definitive Treatment", legend.labs=c("Complete cohort"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.available, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 73 13 0.866 0.0347 0.780 0.920
24 42 3 0.822 0.0412 0.724 0.888
36 18 0 0.822 0.0412 0.724 0.888#Association of Baseline ctDNA MTM levels with clinicopathological factors
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
tally(~cStage, data=circ_data, margins = TRUE)cStage
I/II III/IV Total
34 29 63 circ_data$cStage <- factor(circ_data$cStage, levels = c("I/II","III/IV"), labels = c("I/II (n=34)","III/IV (n=29)"))
boxplot(ctDNA.Base.MTM~cStage, data=circ_data, main="ctDNA pre-treatment MTM - Stage", xlab="Stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.Stage <- circ_data %>%
group_by(cStage) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.Stage)# A tibble: 2 × 2
cStage median_ctDNA_Base_MTM
<fct> <dbl>
1 I/II (n=34) 16.0
2 III/IV (n=29) 5.43m1<-wilcox.test(ctDNA.Base.MTM ~ cStage, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m1)
Wilcoxon rank sum test with continuity correction
data: ctDNA.Base.MTM by cStage
W = 601, p-value = 0.138
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
-1.04003 17.95005
sample estimates:
difference in location
3.110541 rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
tally(~cT.status, data=circ_data, margins = TRUE)cT.status
T0-T2 T3-T4 Total
29 34 63 circ_data$cT.status <- factor(circ_data$cT.status, levels = c("T0-T2","T3-T4"), labels = c("T0-T2 (n=29)","T3-T4 (n=34)"))
boxplot(ctDNA.Base.MTM~cT.status, data=circ_data, main="ctDNA pre-treatment MTM - T stage", xlab="T stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.cT <- circ_data %>%
group_by(cT.status) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cT)# A tibble: 2 × 2
cT.status median_ctDNA_Base_MTM
<fct> <dbl>
1 T0-T2 (n=29) 10.0
2 T3-T4 (n=34) 7.15m2<-wilcox.test(ctDNA.Base.MTM ~ cT.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m2)
Wilcoxon rank sum test with continuity correction
data: ctDNA.Base.MTM by cT.status
W = 489, p-value = 0.9615
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
-7.720014 8.029990
sample estimates:
difference in location
-4.450499e-05 rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
tally(~cT, data=circ_data, margins = TRUE)cT
T0 T1 T2 T3 T4 Total
2 5 22 21 13 63 circ_data$cT <- factor(circ_data$cT, levels = c("T0","T1","T2","T3","T4"))
boxplot(ctDNA.Base.MTM~cT, data=circ_data, main="ctDNA pre-treatment MTM - cT status", xlab="cT status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.cT <- circ_data %>%
group_by(cT) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cT)# A tibble: 5 × 2
cT median_ctDNA_Base_MTM
<fct> <dbl>
1 T0 1.82
2 T1 7.54
3 T2 14.1
4 T3 22.3
5 T4 5.43pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$cT,
p.adjust.method = "none",
exact = FALSE)
print(pairwise_wilcox)
Pairwise comparisons using Wilcoxon rank sum test with continuity correction
data: circ_data$ctDNA.Base.MTM and circ_data$cT
T0 T1 T2 T3
T1 0.85 - - -
T2 0.19 0.83 - -
T3 0.14 0.65 0.76 -
T4 0.55 0.62 0.33 0.23
P value adjustment method: none rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cT <- factor(circ_data$cT, levels = c("T0", "T1", "T2", "T3", "T4"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
cT_levels <- levels(circ_data$cT)
p_value_matrix <- matrix(NA, nrow = length(cT_levels), ncol = length(cT_levels))
rownames(p_value_matrix) <- cT_levels
colnames(p_value_matrix) <- cT_levels
for (i in 1:length(cT_levels)) {
for (j in i:length(cT_levels)) {
if (i != j) {
# Extract data for both groups
data1 <- circ_data %>% filter(cT == cT_levels[i]) %>% pull(ctDNA.Base.MTM)
data2 <- circ_data %>% filter(cT == cT_levels[j]) %>% pull(ctDNA.Base.MTM)
# Perform Wilcoxon test and store p-value
test_result <- wilcox.test(data1, data2, exact = FALSE)
p_value_matrix[i, j] <- test_result$p.value
p_value_matrix[j, i] <- test_result$p.value # Make symmetric
} else {
p_value_matrix[i, j] <- 1 # Self-comparison = 1
}
}
}
p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("cT1", "cT2", "p_value")
p_value_data <- p_value_data %>%
mutate(
significance = case_when(
p_value < 0.001 ~ "***",
p_value < 0.01 ~ "**",
p_value < 0.05 ~ "*",
TRUE ~ ""
)
)
ggplot(p_value_data, aes(x = cT1, y = cT2, fill = p_value)) +
geom_tile(color = "white", size = 0.8) + # Thicker grid lines for separation
geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") + # Significance markers
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) + # Gradient colors
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
axis.text.y = element_text(size = 12, face = "bold"),
panel.grid = element_blank()) +
labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by cT)",
x = "cT Status", y = "cT Status", fill = "P-Value")
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
tally(~cN.status, data=circ_data, margins = TRUE)cN.status
N0 N1-N3 Total
12 51 63 circ_data$cN.status <- factor(circ_data$cN.status, levels = c("N0","N1-N3"), labels = c("N0 (n=12)","N1-N3 (n=51)"))
boxplot(ctDNA.Base.MTM~cN.status, data=circ_data, main="ctDNA pre-treatment MTM - cN status", xlab="cN status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.cN <- circ_data %>%
group_by(cN.status) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cN)# A tibble: 2 × 2
cN.status median_ctDNA_Base_MTM
<fct> <dbl>
1 N0 (n=12) 2.06
2 N1-N3 (n=51) 16.3 m3<-wilcox.test(ctDNA.Base.MTM ~ cN.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m3)
Wilcoxon rank sum test with continuity correction
data: ctDNA.Base.MTM by cN.status
W = 163, p-value = 0.01256
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
-41.019963 -1.190095
sample estimates:
difference in location
-11.93637 rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
tally(~cN, data=circ_data, margins = TRUE)cN
N0 N1 N2 N3 Total
12 22 23 6 63 circ_data$cN <- factor(circ_data$cN, levels = c("N0","N1","N2","N3"))
boxplot(ctDNA.Base.MTM~cN, data=circ_data, main="ctDNA pre-treatment MTM - N Stage", xlab="N Stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 500))
median_ctDNA.cN <- circ_data %>%
group_by(cN) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cN)# A tibble: 4 × 2
cN median_ctDNA_Base_MTM
<fct> <dbl>
1 N0 2.06
2 N1 14.3
3 N2 32.0
4 N3 5.55pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$cN,
p.adjust.method = "none",
exact = FALSE)
print(pairwise_wilcox)
Pairwise comparisons using Wilcoxon rank sum test with continuity correction
data: circ_data$ctDNA.Base.MTM and circ_data$cN
N0 N1 N2
N1 0.0473 - -
N2 0.0074 0.3513 -
N3 0.3736 0.9777 0.7262
P value adjustment method: none rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cN <- factor(circ_data$cN, levels = c("N0","N1","N2","N3"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
cN_levels <- levels(circ_data$cN)
p_value_matrix <- matrix(NA, nrow = length(cN_levels), ncol = length(cN_levels))
rownames(p_value_matrix) <- cN_levels
colnames(p_value_matrix) <- cN_levels
for (i in 1:length(cN_levels)) {
for (j in i:length(cN_levels)) {
if (i != j) {
# Extract data for both groups
data1 <- circ_data %>% filter(cN == cN_levels[i]) %>% pull(ctDNA.Base.MTM)
data2 <- circ_data %>% filter(cN == cN_levels[j]) %>% pull(ctDNA.Base.MTM)
# Perform Wilcoxon test and store p-value
test_result <- wilcox.test(data1, data2, exact = FALSE)
p_value_matrix[i, j] <- test_result$p.value
p_value_matrix[j, i] <- test_result$p.value # Make symmetric
} else {
p_value_matrix[i, j] <- 1 # Self-comparison = 1
}
}
}
p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("cN1", "cN2", "p_value")
p_value_data <- p_value_data %>%
mutate(
significance = case_when(
p_value < 0.001 ~ "***",
p_value < 0.01 ~ "**",
p_value < 0.05 ~ "*",
TRUE ~ ""
)
)
ggplot(p_value_data, aes(x = cN1, y = cN2, fill = p_value)) +
geom_tile(color = "white", size = 0.8) + # Thicker grid lines for separation
geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") + # Significance markers
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) + # Gradient colors
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
axis.text.y = element_text(size = 12, face = "bold"),
panel.grid = element_blank()) +
labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by cN)",
x = "Status", y = "cN Status", fill = "P-Value")
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cT <- factor(circ_data$cT, levels = c("T0", "T1", "T2", "T3", "T4"))
circ_data$cN <- factor(circ_data$cN, levels = c("N0", "N1", "N2", "N3"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
median_ctDNA <- circ_data %>%
group_by(cT, cN) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE)) %>%
ungroup()
p_value_matrix <- dcast(median_ctDNA, cT ~ cN, value.var = "median_ctDNA_Base_MTM")
p_value_data <- melt(p_value_matrix, id.vars = "cT", variable.name = "cN", value.name = "median_value")
p_value_data$missing <- ifelse(is.na(p_value_data$median_value), "Missing", "Present")
p_value_data$median_value[is.na(p_value_data$median_value)] <- 0
ggplot(p_value_data, aes(x = cN, y = cT, fill = median_value)) +
geom_tile(color = "black", size = 0.5) + # Black gridlines for separation
geom_text(aes(label = round(median_value, 2)), color = "black", size = 5) + # Display median values
scale_fill_gradient(low = "white", high = "blue") + # Color gradient similar to the reference image
theme_minimal() +
theme(axis.text.x = element_text(size = 12, face = "bold"),
axis.text.y = element_text(size = 12, face = "bold"),
panel.grid = element_blank()) +
labs(title = "Median ctDNA.Base.MTM by cT and cN",
x = "cN Status", y = "cT Status", fill = "Median MTM") +
geom_tile(data = subset(p_value_data, missing == "Missing"),
aes(x = cN, y = cT), color = "black", fill = NA, size = 0.5, linetype = "dashed") # Add diagonal cross for missing values
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
tally(~cM, data=circ_data, margins = TRUE)cM
M0 M1 Total
61 2 63 circ_data$cM <- factor(circ_data$cM, levels = c("M0","M1"), labels = c("M0 (n=61)","M1 (n=2)"))
boxplot(ctDNA.Base.MTM~cM, data=circ_data, main="ctDNA pre-treatment MTM - cM", xlab="cM", ylab="MTM/mL", col="white",border="black", ylim = c(0, 500))
median_ctDNA.cM <- circ_data %>%
group_by(cM) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cM)# A tibble: 2 × 2
cM median_ctDNA_Base_MTM
<fct> <dbl>
1 M0 (n=61) 8.84
2 M1 (n=2) 235. m4<-wilcox.test(ctDNA.Base.MTM ~ cM, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m4)
Wilcoxon rank sum test with continuity correction
data: ctDNA.Base.MTM by cM
W = 54, p-value = 0.7987
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
-469.97996 76.10995
sample estimates:
difference in location
-0.07005084 rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
tally(~p16.status, data=circ_data, margins = TRUE)p16.status
Negative Positive Total
24 39 63 circ_data$p16.status <- factor(circ_data$p16.status, levels = c("Negative","Positive"), labels = c("p16 neg (n=24)","p16 pos (n=39)"))
boxplot(ctDNA.Base.MTM~p16.status, data=circ_data, main="ctDNA pre-treatment MTM - p16 status", xlab="p16 status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.p16 <- circ_data %>%
group_by(p16.status) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.p16)# A tibble: 2 × 2
p16.status median_ctDNA_Base_MTM
<fct> <dbl>
1 p16 neg (n=24) 2.82
2 p16 pos (n=39) 16.3 m5<-wilcox.test(ctDNA.Base.MTM ~ p16.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m5)
Wilcoxon rank sum test with continuity correction
data: ctDNA.Base.MTM by p16.status
W = 294, p-value = 0.014
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
-27.0900146 -0.8600542
sample estimates:
difference in location
-8.507328 rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
tally(~Prim.Location, data=circ_data, margins = TRUE)Prim.Location
Larynx/Hypopharynx Oral cavity Oropharynx Other (paranasal sinus and nasopharyngeal)
3 8 47 5
Total
63 circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Larynx/Hypopharynx","Oral cavity", "Oropharynx", "Other (paranasal sinus and nasopharyngeal)"))
boxplot(ctDNA.Base.MTM~Prim.Location, data=circ_data, main="ctDNA pre-treatment MTM - Tumor Location", xlab="Tumor Location", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.loc <- circ_data %>%
group_by(Prim.Location) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.loc)# A tibble: 4 × 2
Prim.Location median_ctDNA_Base_MTM
<fct> <dbl>
1 Larynx/Hypopharynx 2.08
2 Oral cavity 2.81
3 Oropharynx 15.8
4 Other (paranasal sinus and nasopharyngeal) 5.43pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$Prim.Location,
p.adjust.method = "none",
exact = FALSE)
print(pairwise_wilcox)
Pairwise comparisons using Wilcoxon rank sum test with continuity correction
data: circ_data$ctDNA.Base.MTM and circ_data$Prim.Location
Larynx/Hypopharynx Oral cavity Oropharynx
Oral cavity 0.92 - -
Oropharynx 0.25 0.12 -
Other (paranasal sinus and nasopharyngeal) 0.55 0.77 0.65
P value adjustment method: none rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Larynx/Hypopharynx","Oral cavity", "Oropharynx", "Other (paranasal sinus and nasopharyngeal)"), labels = c("LRX/HPRX","OC","PRX","Other"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
pl_levels <- levels(circ_data$Prim.Location)
p_value_matrix <- matrix(NA, nrow = length(pl_levels), ncol = length(pl_levels))
rownames(p_value_matrix) <- pl_levels
colnames(p_value_matrix) <- pl_levels
for (i in 1:length(pl_levels)) {
for (j in i:length(pl_levels)) {
if (i != j) {
# Extract data for both groups
data1 <- circ_data %>% filter(Prim.Location == pl_levels[i]) %>% pull(ctDNA.Base.MTM)
data2 <- circ_data %>% filter(Prim.Location == pl_levels[j]) %>% pull(ctDNA.Base.MTM)
# Perform Wilcoxon test and store p-value
test_result <- wilcox.test(data1, data2, exact = FALSE)
p_value_matrix[i, j] <- test_result$p.value
p_value_matrix[j, i] <- test_result$p.value # Make symmetric
} else {
p_value_matrix[i, j] <- 1 # Self-comparison = 1
}
}
}
p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("pl1", "pl2", "p_value")
p_value_data <- p_value_data %>%
mutate(
significance = case_when(
p_value < 0.001 ~ "***",
p_value < 0.01 ~ "**",
p_value < 0.05 ~ "*",
TRUE ~ ""
)
)
ggplot(p_value_data, aes(x = pl1, y = pl2, fill = p_value)) +
geom_tile(color = "white", size = 0.8) + # Thicker grid lines for separation
geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") + # Significance markers
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) + # Gradient colors
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
axis.text.y = element_text(size = 12, face = "bold"),
panel.grid = element_blank()) +
labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by Tumor Location)",
x = "Tumor Location", y = "Tumor Location", fill = "P-Value")
#PFS by ctDNA status at MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 56 9 NA 40.08 NA
ctDNA.MRD=POSITIVE 13 8 15.5 4.21 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)# A tibble: 2 × 5
ctDNA.MRD Total Events Fraction Percentage
<chr> <int> <int> <dbl> <dbl>
1 NEGATIVE 56 9 0.161 16.1
2 POSITIVE 13 8 0.615 61.5surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 56 0 1.000 0.0000 1.000 1.000
12 44 5 0.910 0.0384 0.797 0.962
24 27 3 0.841 0.0523 0.705 0.918
36 9 0 0.841 0.0523 0.705 0.918
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 13 0 1.000 0.000 1.0000 1.000
12 6 6 0.538 0.138 0.2477 0.760
24 2 2 0.323 0.144 0.0862 0.594
36 2 0 0.323 0.144 0.0862 0.594circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 69, number of events= 17
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 1.8545 6.3886 0.5021 3.693 0.000221 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 6.389 0.1565 2.388 17.09
Concordance= 0.69 (se = 0.059 )
Likelihood ratio test= 12.07 on 1 df, p=5e-04
Wald test = 13.64 on 1 df, p=2e-04
Score (logrank) test = 17.94 on 1 df, p=2e-05cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 6.39 (2.39-17.09); p = 0"circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 9.4257, df = 1, p-value = 0.00214fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.001816
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.839047 39.292099
sample estimates:
odds ratio
8.005348 print(contingency_table)
No Progression Progression
Negative 47 9
Positive 5 8table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at MRD",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#OS by ctDNA status at MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 56 1 NA NA NA
ctDNA.MRD=POSITIVE 13 5 NA 12.3 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)# A tibble: 2 × 5
ctDNA.MRD Total Events Fraction Percentage
<chr> <int> <int> <dbl> <dbl>
1 NEGATIVE 56 1 0.0179 1.79
2 POSITIVE 13 5 0.385 38.5 surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA at MRD", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 48 1 0.982 0.0177 0.88 0.997
24 30 0 0.982 0.0177 0.88 0.997
36 11 0 0.982 0.0177 0.88 0.997
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 8 3 0.769 0.117 0.442 0.919
24 4 2 0.561 0.153 0.233 0.795
36 3 0 0.561 0.153 0.233 0.795circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 69, number of events= 6
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 3.247 25.718 1.096 2.962 0.00306 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 25.72 0.03888 3 220.5
Concordance= 0.832 (se = 0.081 )
Likelihood ratio test= 13.07 on 1 df, p=3e-04
Wald test = 8.77 on 1 df, p=0.003
Score (logrank) test = 19.67 on 1 df, p=9e-06cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 25.72 (3-220.48); p = 0.003"circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 13.554, df = 1, p-value = 0.0002318fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.0006155
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
3.015475 1634.641331
sample estimates:
odds ratio
31.44433 print(contingency_table)
Alive Deceased
Negative 55 1
Positive 8 5table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at MRD",
x = "ctDNA",
y = "Patients (%)",
fill = "Living Status",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#PFS by ctDNA status at MRD - exclude pts with adjuvant treatment post-MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
excluded_ids <- c("UNM-007", "UNM-008", "UNM-023", "UNM-027", "UNM-029",
"UNM-030", "UNM-035", "UNM-045", "UNM-051", "UNM-059",
"UNM-075", "UNM-082", "UNM-032", "UNM-042", "UNM-043",
"UNM-048", "UNM-050", "UNM-070")
circ_data <- circ_data[!circ_data$PatientID %in% excluded_ids, ]
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 44 7 NA 40.08 NA
ctDNA.MRD=POSITIVE 7 6 11.3 3.12 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)# A tibble: 2 × 5
ctDNA.MRD Total Events Fraction Percentage
<chr> <int> <int> <dbl> <dbl>
1 NEGATIVE 44 7 0.159 15.9
2 POSITIVE 7 6 0.857 85.7surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 44 0 1.000 0.0000 1.000 1.000
12 37 3 0.932 0.0380 0.803 0.977
24 21 3 0.847 0.0582 0.688 0.929
36 8 0 0.847 0.0582 0.688 0.929
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 7 0 1.000 0.000 1.00000 1.000
12 3 4 0.429 0.187 0.09775 0.734
24 1 2 0.143 0.132 0.00712 0.465
36 1 0 0.143 0.132 0.00712 0.465circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 51, number of events= 13
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.3109 10.0834 0.5805 3.981 6.87e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 10.08 0.09917 3.232 31.46
Concordance= 0.71 (se = 0.067 )
Likelihood ratio test= 13.27 on 1 df, p=3e-04
Wald test = 15.85 on 1 df, p=7e-05
Score (logrank) test = 24.07 on 1 df, p=9e-07cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 10.08 (3.23-31.46); p = 0"circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 12.037, df = 1, p-value = 0.0005216fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.0005781
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
2.866206 1480.826861
sample estimates:
odds ratio
28.65583 print(contingency_table)
No Progression Progression
Negative 37 7
Positive 1 6table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at MRD",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#OS by ctDNA status at MRD - exclude pts with adjuvant treatment post-MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
excluded_ids <- c("UNM-007", "UNM-008", "UNM-023", "UNM-027", "UNM-029",
"UNM-030", "UNM-035", "UNM-045", "UNM-051", "UNM-059",
"UNM-075", "UNM-082", "UNM-032", "UNM-042", "UNM-043",
"UNM-048", "UNM-050", "UNM-070")
circ_data <- circ_data[!circ_data$PatientID %in% excluded_ids, ]
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 44 1 NA NA NA
ctDNA.MRD=POSITIVE 7 3 NA 12.3 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)# A tibble: 2 × 5
ctDNA.MRD Total Events Fraction Percentage
<chr> <int> <int> <dbl> <dbl>
1 NEGATIVE 44 1 0.0227 2.27
2 POSITIVE 7 3 0.429 42.9 surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA at MRD", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 39 1 0.977 0.0225 0.849 0.997
24 23 0 0.977 0.0225 0.849 0.997
36 9 0 0.977 0.0225 0.849 0.997
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 5 1 0.857 0.132 0.334 0.979
24 3 2 0.514 0.204 0.118 0.813
36 2 0 0.514 0.204 0.118 0.813circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 51, number of events= 4
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 3.026 20.613 1.155 2.619 0.00881 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 20.61 0.04851 2.142 198.4
Concordance= 0.799 (se = 0.119 )
Likelihood ratio test= 8.14 on 1 df, p=0.004
Wald test = 6.86 on 1 df, p=0.009
Score (logrank) test = 13.95 on 1 df, p=2e-04cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 20.61 (2.14-198.38); p = 0.009"circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 8.7198, df = 1, p-value = 0.003148fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.006303
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.805221 1704.546058
sample estimates:
odds ratio
27.80596 print(contingency_table)
Alive Deceased
Negative 43 1
Positive 4 3table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at MRD",
x = "ctDNA",
y = "Patients (%)",
fill = "Living Status",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#PFS by ctDNA status at MRD Stage I/II
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="I/II",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 29 3 40.1 NA NA
ctDNA.MRD=POSITIVE 5 2 NA 6.01 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)# A tibble: 2 × 5
ctDNA.MRD Total Events Fraction Percentage
<chr> <int> <int> <dbl> <dbl>
1 NEGATIVE 29 3 0.103 10.3
2 POSITIVE 5 2 0.4 40 surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD Stage I/II", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 29 0 1.000 0.0000 1.000 1.000
12 25 1 0.966 0.0339 0.779 0.995
24 16 1 0.925 0.0510 0.732 0.981
36 4 0 0.925 0.0510 0.732 0.981
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1.0 0.000 1.000 1.000
12 2 2 0.6 0.219 0.126 0.882
24 1 0 0.6 0.219 0.126 0.882
36 1 0 0.6 0.219 0.126 0.882circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 34, number of events= 5
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.286 9.838 1.035 2.209 0.0272 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 9.838 0.1016 1.294 74.78
Concordance= 0.725 (se = 0.118 )
Likelihood ratio test= 4.21 on 1 df, p=0.04
Wald test = 4.88 on 1 df, p=0.03
Score (logrank) test = 7.19 on 1 df, p=0.007cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 9.84 (1.29-74.78); p = 0.027"circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 1.0932, df = 1, p-value = 0.2958fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.1464
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.326544 73.672748
sample estimates:
odds ratio
5.350976 print(contingency_table)
No Progression Progression
Negative 26 3
Positive 3 2table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at MRD Stage I/II",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#PFS by ctDNA status at MRD Stage III/IV
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="III/IV",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 27 6 NA NA NA
ctDNA.MRD=POSITIVE 8 6 13.4 4.21 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)# A tibble: 2 × 5
ctDNA.MRD Total Events Fraction Percentage
<chr> <int> <int> <dbl> <dbl>
1 NEGATIVE 27 6 0.222 22.2
2 POSITIVE 8 6 0.75 75 surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD Stage III/IV", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 27 0 1.000 0.0000 1.000 1.000
12 19 4 0.850 0.0691 0.649 0.941
24 11 2 0.753 0.0892 0.526 0.882
36 5 0 0.753 0.0892 0.526 0.882
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 8 0 1.00 0.000 1.0000 1.000
12 4 4 0.50 0.177 0.1520 0.775
24 1 2 0.25 0.153 0.0371 0.558
36 1 0 0.25 0.153 0.0371 0.558circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 35, number of events= 12
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 1.4899 4.4365 0.5788 2.574 0.01 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 4.437 0.2254 1.427 13.79
Concordance= 0.663 (se = 0.07 )
Likelihood ratio test= 6.1 on 1 df, p=0.01
Wald test = 6.63 on 1 df, p=0.01
Score (logrank) test = 7.93 on 1 df, p=0.005cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 4.44 (1.43-13.79); p = 0.01"circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 5.4671, df = 1, p-value = 0.01938fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.01073
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.308188 121.976548
sample estimates:
odds ratio
9.642373 print(contingency_table)
No Progression Progression
Negative 21 6
Positive 2 6table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at MRD Stage I/II",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#PFS by ctDNA at MRD p16(+)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Positive",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 29 2 NA NA NA
ctDNA.MRD=POSITIVE 8 4 22.2 6.01 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)# A tibble: 2 × 5
ctDNA.MRD Total Events Fraction Percentage
<chr> <int> <int> <dbl> <dbl>
1 NEGATIVE 29 2 0.0690 6.90
2 POSITIVE 8 4 0.5 50 surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD p16(+)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 29 0 1.000 0.0000 1.000 1.000
12 23 1 0.966 0.0339 0.779 0.995
24 15 1 0.920 0.0553 0.711 0.980
36 4 0 0.920 0.0553 0.711 0.980
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 8 0 1.000 0.000 1.000 1.000
12 4 3 0.625 0.171 0.229 0.861
24 2 1 0.417 0.205 0.072 0.747
36 2 0 0.417 0.205 0.072 0.747circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 37, number of events= 6
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.3218 10.1936 0.8706 2.667 0.00766 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 10.19 0.0981 1.85 56.16
Concordance= 0.767 (se = 0.09 )
Likelihood ratio test= 7.47 on 1 df, p=0.006
Wald test = 7.11 on 1 df, p=0.008
Score (logrank) test = 10.81 on 1 df, p=0.001cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 10.19 (1.85-56.16); p = 0.008"circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 5.6953, df = 1, p-value = 0.01701fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.01294
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.281882 176.017338
sample estimates:
odds ratio
12.07276 print(contingency_table)
No Progression Progression
Negative 27 2
Positive 4 4table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at MRD p16(+)",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#PFS by ctDNA at MRD p16(-)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Negative",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 27 7 40.1 40.08 NA
ctDNA.MRD=POSITIVE 5 4 11.3 4.21 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)# A tibble: 2 × 5
ctDNA.MRD Total Events Fraction Percentage
<chr> <int> <int> <dbl> <dbl>
1 NEGATIVE 27 7 0.259 25.9
2 POSITIVE 5 4 0.8 80 surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD p16(-)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 27 0 1.000 0.0000 1.000 1.000
12 21 4 0.850 0.0691 0.649 0.941
24 12 2 0.762 0.0858 0.542 0.886
36 5 0 0.762 0.0858 0.542 0.886
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1.0 0.000 1.000 1.000
12 2 3 0.4 0.219 0.052 0.753circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 32, number of events= 11
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 1.7286 5.6328 0.6515 2.653 0.00797 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 5.633 0.1775 1.571 20.2
Concordance= 0.655 (se = 0.072 )
Likelihood ratio test= 5.79 on 1 df, p=0.02
Wald test = 7.04 on 1 df, p=0.008
Score (logrank) test = 8.93 on 1 df, p=0.003cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 5.63 (1.57-20.2); p = 0.008"circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 3.3339, df = 1, p-value = 0.06787fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.03671
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.8530265 587.6303377
sample estimates:
odds ratio
10.45501 print(contingency_table)
No Progression Progression
Negative 20 7
Positive 1 4table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at MRD p16(-)",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#PFS by ctDNA status at surveillance
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 55 2 NA NA NA
ctDNA.Surveillance=POSITIVE 24 22 12 8.77 20.5event_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)# A tibble: 2 × 5
ctDNA.Surveillance Total Events Fraction Percentage
<chr> <int> <int> <dbl> <dbl>
1 NEGATIVE 55 2 0.0364 3.64
2 POSITIVE 24 22 0.917 91.7 surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 55 0 1.000 0.0000 1.000 1.000
12 44 2 0.964 0.0252 0.862 0.991
24 25 0 0.964 0.0252 0.862 0.991
36 9 0 0.964 0.0252 0.862 0.991
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 24 0 1.000 0.0000 1.0000 1.000
12 12 12 0.500 0.1021 0.2910 0.678
24 5 7 0.208 0.0829 0.0759 0.385
36 2 2 0.125 0.0675 0.0314 0.287circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 79, number of events= 24
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 3.6602 38.8674 0.7404 4.944 7.67e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 38.87 0.02573 9.107 165.9
Concordance= 0.836 (se = 0.038 )
Likelihood ratio test= 53.02 on 1 df, p=3e-13
Wald test = 24.44 on 1 df, p=8e-07
Score (logrank) test = 65.38 on 1 df, p=6e-16cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 38.87 (9.11-165.88); p = 0"circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 57.128, df = 1, p-value = 4.083e-14fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 3.621e-15
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
31.4353 3628.7424
sample estimates:
odds ratio
229.7243 print(contingency_table)
No Progression Progression
Negative 53 2
Positive 2 22table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at Surveillance",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#OS by ctDNA status at surveillance
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 55 1 NA NA NA
ctDNA.Surveillance=POSITIVE 24 6 NA NA NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)# A tibble: 2 × 5
ctDNA.Surveillance Total Events Fraction Percentage
<chr> <int> <int> <dbl> <dbl>
1 NEGATIVE 55 1 0.0182 1.82
2 POSITIVE 24 6 0.25 25 surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA at Surveillance", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 55 0 1.000 0.000 1.000 1.000
12 45 1 0.982 0.018 0.878 0.997
24 25 0 0.982 0.018 0.878 0.997
36 9 0 0.982 0.018 0.878 0.997
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 24 0 1.000 0.0000 1.000 1.000
12 20 3 0.875 0.0675 0.661 0.958
24 11 3 0.715 0.1015 0.464 0.864
36 7 0 0.715 0.1015 0.464 0.864circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 79, number of events= 7
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 2.665 14.369 1.080 2.467 0.0136 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 14.37 0.0696 1.729 119.4
Concordance= 0.776 (se = 0.075 )
Likelihood ratio test= 9.62 on 1 df, p=0.002
Wald test = 6.09 on 1 df, p=0.01
Score (logrank) test = 10.65 on 1 df, p=0.001cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 14.37 (1.73-119.39); p = 0.014"#Median numbers of time points in the longitudinal setting
# Load the dataset
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
median_Nsurvtps <- median(circ_datadf$Nsurvtps, na.rm = TRUE)
min_Nsurvtps <- min(circ_datadf$Nsurvtps, na.rm = TRUE)
max_Nsurvtps <- max(circ_datadf$Nsurvtps, na.rm = TRUE)
cat(sprintf("Median # of surveillance time points: %d (%d-%d)\n",
median_Nsurvtps, min_Nsurvtps, max_Nsurvtps))Median # of surveillance time points: 4 (1-20)#Time-dependent analysis for PFS in longitudinal time points
rm(list=ls())
setwd("~/Downloads")
dt <- read_xlsx("CLIA HNSCC Peddada Clinical Data_Time dependent.xlsx") |>
clean_names() |>
mutate(across(.cols = c(window_start_date,dfs_date,
surveillance_1_date:surveillance_13_date),
.fns = ~ as_date(as.Date(.x, format = "%Y-%m-%d"))))
dt_biomarker <- dt |>
select(pts_id, ct_dna_surveillance_available,
window_start_date,
surveillance_1_status:surveillance_13_date) |>
filter(ct_dna_surveillance_available) |>
pivot_longer(cols = surveillance_1_status:surveillance_13_date,
names_to = c("visit_number", ".value"),
names_pattern = "surveillance_(.)_(.*)") |>
mutate(biomarker_time = day(days(date - window_start_date))) |>
select(pts_id, biomarker_time, biomarker_status = status) |>
filter(!is.na(biomarker_time))
glimpse(dt_biomarker)Rows: 332
Columns: 3
$ pts_id <chr> "UNM-002", "UNM-003", "UNM-004", "UNM-004", "UNM-004", "UNM-004", "UNM-004", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-0…
$ biomarker_time <dbl> 2, 14, 17, 108, 197, 240, 269, 15, 55, 168, 244, 326, 412, 46, 136, 225, 313, 417, 508, 597, 35, 246, 112, 202, 293, 386, 477, 571, 29, 99, 186, 267,…
$ biomarker_status <chr> "NEGATIVE", "POSITIVE", "NEGATIVE", "NEGATIVE", "POSITIVE", "POSITIVE", "POSITIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", NA, "…dt_survival <- dt |>
select(pts_id, ct_dna_surveillance_available,
window_start_date:dfs_date, dfs_event) |> # Added dfs_event here
filter(ct_dna_surveillance_available) |>
mutate(dfs_time = (dfs_date - window_start_date),
dfs_time = day(days(dfs_time)),
dfs_event = as.numeric(dfs_event)) |>
select(pts_id, dfs_time, dfs_event)
glimpse(dt_survival)Rows: 79
Columns: 3
$ pts_id <chr> "UNM-002", "UNM-003", "UNM-004", "UNM-008", "UNM-009", "UNM-014", "UNM-016", "UNM-017", "UNM-018", "UNM-019", "UNM-020", "UNM-021", "UNM-022", "UNM-023", "U…
$ dfs_time <dbl> 977, 53, 398, 1043, 603, 298, 745, 402, 1148, 1155, 987, 170, 1016, 237, 535, 970, 728, 934, 1324, 989, 437, 203, 656, 647, 625, 48, 1033, 1159, 108, 591, 7…
$ dfs_event <dbl> 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, …aux <- dt_survival %>%
filter(dfs_time <= 0)
tab <- left_join(aux, dt) |>
select(pts_id, window_start_date, dfs_time, dfs_date,
surveillance_1_date:surveillance_14_date) |>
mutate(across(.cols = dfs_date:surveillance_14_date,
.fns = ~ as_date(.x))) |>
select(pts_id, window_start_date, dfs_date, dfs_time)
datatable(tab, filter = "top")
dt_survival <- dt_survival |>
filter(dfs_time > 0)
aux <- dt |>
select(pts_id, ct_dna_surveillance_available,
window_start_date, dfs_date,
surveillance_1_date:surveillance_14_date) |>
mutate(across(.cols = surveillance_1_date:surveillance_14_date,
.fns = ~ .x - window_start_date)) |>
mutate(across(.cols = surveillance_1_date:surveillance_14_date,
.fns = ~ .x < 0)) |>
rowwise() |>
mutate(sum_neg =
sum(c_across(surveillance_1_date:surveillance_14_date),
na.rm = TRUE)) |>
select(pts_id, sum_neg)
tab <- left_join(aux, dt) |>
filter(sum_neg > 0) |>
select(pts_id, sum_neg, window_start_date,
surveillance_1_date:surveillance_14_date) |>
mutate(across(.cols = window_start_date:surveillance_14_date,
.fns = ~ as_date(.x)))
datatable(tab, filter = "top")
aux <- dt |>
select(pts_id, ct_dna_surveillance_available,
window_start_date, dfs_date,
surveillance_1_date:surveillance_14_date) |>
mutate(across(.cols = dfs_date:surveillance_14_date,
.fns = ~ .x - window_start_date)) |>
mutate(across(.cols = surveillance_2_date:surveillance_14_date,
.fns = ~ dfs_date < .x)) |>
rowwise() |>
mutate(n_biomarker_after_event = sum(c_across(surveillance_2_date:
surveillance_14_date),
na.rm = TRUE)) |>
mutate(across(.cols = surveillance_1_date:surveillance_14_date,
.fns = ~ !is.na(.x))) |>
mutate(total_biomarker = sum(c_across(surveillance_2_date:
surveillance_14_date),
na.rm = TRUE)) |>
select(pts_id, n_biomarker_after_event, total_biomarker)
temp <- aux |>
select(-pts_id) |>
group_by(n_biomarker_after_event, total_biomarker) |> # Direct grouping
summarise(freq = n(), .groups = "drop") # Drop groups after summarization
tab <- left_join(aux, dt) |>
select(pts_id, n_biomarker_after_event, total_biomarker,
dfs_date,
surveillance_2_date:surveillance_14_date) |>
mutate(across(.cols = dfs_date:surveillance_14_date,
.fns = ~ as_date(.x))) |>
filter(n_biomarker_after_event > 0)
datatable(tab, filter = "top")
aux <- tmerge(data1 = dt_survival,
data2 = dt_survival,
id = pts_id,
dfs_event = event(dfs_time, dfs_event))
dt_final <- tmerge(data1 = aux,
data2 = dt_biomarker,
id = pts_id,
biomarker_status =
tdc(biomarker_time, biomarker_status))
datatable(dt_final, filter = "top")
# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
# data = dt_final)
# summary(fit)
fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)
summary(fit)Call:
coxph(formula = Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)
n= 327, number of events= 24
(74 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
biomarker_statusPOSITIVE 4.5321 92.9506 0.7454 6.08 1.2e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE 92.95 0.01076 21.57 400.6
Concordance= 0.896 (se = 0.036 )
Likelihood ratio test= 80.94 on 1 df, p=<2e-16
Wald test = 36.97 on 1 df, p=1e-09
Score (logrank) test = 152.1 on 1 df, p=<2e-16cox_fit_summary <- summary(fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 92.95 (21.57-400.63); p = 0"#Time-dependent analysis for OS in longitudinal time points
rm(list=ls())
setwd("~/Downloads")
dt <- read_xlsx("CLIA HNSCC Peddada Clinical Data_Time dependent.xlsx") |>
clean_names() |>
mutate(across(.cols = c(window_start_date,os_date,
surveillance_1_date:surveillance_13_date),
.fns = ~ as_date(as.Date(.x, format = "%Y-%m-%d"))))
dt_biomarker <- dt |>
select(pts_id, ct_dna_surveillance_available,
window_start_date,
surveillance_1_status:surveillance_13_date) |>
filter(ct_dna_surveillance_available) |>
pivot_longer(cols = surveillance_1_status:surveillance_13_date,
names_to = c("visit_number", ".value"),
names_pattern = "surveillance_(.)_(.*)") |>
mutate(biomarker_time = day(days(date - window_start_date))) |>
select(pts_id, biomarker_time, biomarker_status = status) |>
filter(!is.na(biomarker_time))
glimpse(dt_biomarker)Rows: 332
Columns: 3
$ pts_id <chr> "UNM-002", "UNM-003", "UNM-004", "UNM-004", "UNM-004", "UNM-004", "UNM-004", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-0…
$ biomarker_time <dbl> 2, 14, 17, 108, 197, 240, 269, 15, 55, 168, 244, 326, 412, 46, 136, 225, 313, 417, 508, 597, 35, 246, 112, 202, 293, 386, 477, 571, 29, 99, 186, 267,…
$ biomarker_status <chr> "NEGATIVE", "POSITIVE", "NEGATIVE", "NEGATIVE", "POSITIVE", "POSITIVE", "POSITIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", NA, "…dt_survival <- dt |>
select(pts_id, ct_dna_surveillance_available,
window_start_date:os_date, os_event) |> # Added os_event here
filter(ct_dna_surveillance_available) |>
mutate(dfs_time = (os_date - window_start_date),
dfs_time = day(days(dfs_time)),
os_event = as.numeric(os_event)) |>
select(pts_id, dfs_time, os_event)
glimpse(dt_survival)Rows: 79
Columns: 3
$ pts_id <chr> "UNM-002", "UNM-003", "UNM-004", "UNM-008", "UNM-009", "UNM-014", "UNM-016", "UNM-017", "UNM-018", "UNM-019", "UNM-020", "UNM-021", "UNM-022", "UNM-023", "UN…
$ dfs_time <dbl> 977, 53, 360, 1043, 603, 298, 745, 948, 1148, 1155, 987, 423, 1016, 1093, 535, 970, 728, 934, 1324, 989, 437, 987, 1603, 647, 1108, 1346, 1033, 1159, 336, 59…
$ os_event <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1…aux <- dt_survival %>%
filter(dfs_time <= 0)
tab <- left_join(aux, dt) |>
select(pts_id, window_start_date, dfs_time, os_date,
surveillance_1_date:surveillance_14_date) |>
mutate(across(.cols = os_date:surveillance_14_date,
.fns = ~ as_date(.x))) |>
select(pts_id, window_start_date, os_date, dfs_time)
datatable(tab, filter = "top")
dt_survival <- dt_survival |>
filter(dfs_time > 0)
aux <- dt |>
select(pts_id, ct_dna_surveillance_available,
window_start_date, dfs_date,
surveillance_1_date:surveillance_14_date) |>
mutate(across(.cols = surveillance_1_date:surveillance_14_date,
.fns = ~ .x - window_start_date)) |>
mutate(across(.cols = surveillance_1_date:surveillance_14_date,
.fns = ~ .x < 0)) |>
rowwise() |>
mutate(sum_neg =
sum(c_across(surveillance_1_date:surveillance_14_date),
na.rm = TRUE)) |>
select(pts_id, sum_neg)
tab <- left_join(aux, dt) |>
filter(sum_neg > 0) |>
select(pts_id, sum_neg, window_start_date,
surveillance_1_date:surveillance_14_date) |>
mutate(across(.cols = window_start_date:surveillance_14_date,
.fns = ~ as_date(.x)))
datatable(tab, filter = "top")
aux <- dt |>
select(pts_id, ct_dna_surveillance_available,
window_start_date, dfs_date,
surveillance_1_date:surveillance_14_date) |>
mutate(across(.cols = dfs_date:surveillance_14_date,
.fns = ~ .x - window_start_date)) |>
mutate(across(.cols = surveillance_2_date:surveillance_14_date,
.fns = ~ dfs_date < .x)) |>
rowwise() |>
mutate(n_biomarker_after_event = sum(c_across(surveillance_2_date:
surveillance_14_date),
na.rm = TRUE)) |>
mutate(across(.cols = surveillance_1_date:surveillance_14_date,
.fns = ~ !is.na(.x))) |>
mutate(total_biomarker = sum(c_across(surveillance_2_date:
surveillance_14_date),
na.rm = TRUE)) |>
select(pts_id, n_biomarker_after_event, total_biomarker)
temp <- aux |>
select(-pts_id) |>
group_by(n_biomarker_after_event, total_biomarker) |> # Direct grouping
summarise(freq = n(), .groups = "drop") # Drop groups after summarization
tab <- left_join(aux, dt) |>
select(pts_id, n_biomarker_after_event, total_biomarker,
dfs_date,
surveillance_2_date:surveillance_14_date) |>
mutate(across(.cols = dfs_date:surveillance_14_date,
.fns = ~ as_date(.x))) |>
filter(n_biomarker_after_event > 0)
datatable(tab, filter = "top")
aux <- tmerge(data1 = dt_survival,
data2 = dt_survival,
id = pts_id,
os_event = event(dfs_time, os_event))
dt_final <- tmerge(data1 = aux,
data2 = dt_biomarker,
id = pts_id,
biomarker_status =
tdc(biomarker_time, biomarker_status))
datatable(dt_final, filter = "top")
# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, os_event) ~ biomarker_status,
# data = dt_final)
# summary(fit)
fit <- coxph(Surv(tstart, tstop, os_event) ~ biomarker_status,
data = dt_final)
summary(fit)Call:
coxph(formula = Surv(tstart, tstop, os_event) ~ biomarker_status,
data = dt_final)
n= 328, number of events= 7
(74 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
biomarker_statusPOSITIVE 3.251 25.812 1.084 2.999 0.00271 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE 25.81 0.03874 3.085 216
Concordance= 0.821 (se = 0.081 )
Likelihood ratio test= 14.52 on 1 df, p=1e-04
Wald test = 9 on 1 df, p=0.003
Score (logrank) test = 19.82 on 1 df, p=9e-06cox_fit_summary <- summary(fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 25.81 (3.08-215.98); p = 0.003"#PFS by ctDNA status at surveillance Stage I/II
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="I/II",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 32 0 NA NA NA
ctDNA.Surveillance=POSITIVE 10 9 10.7 6.01 NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)# A tibble: 2 × 5
ctDNA.Surveillance Total Events Fraction Percentage
<chr> <int> <int> <dbl> <dbl>
1 NEGATIVE 32 0 0 0
2 POSITIVE 10 9 0.9 90surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = TRUE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance Stage I/II", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 32 0 1 0 1 1
12 26 0 1 0 1 1
24 15 0 1 0 1 1
36 5 0 1 0 1 1
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 10 0 1.0 0.000 1.0000 1.000
12 5 5 0.5 0.158 0.1836 0.753
24 3 2 0.3 0.145 0.0711 0.578
36 1 1 0.2 0.126 0.0309 0.475circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxphf(surv_object ~ ctDNA.Surveillance, data=circ_data)
summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.SurveillancePOSITIVE 4.386253 1.541238 80.33884 10.04137 10388.64 26.61378 2.48465e-07
Likelihood ratio test=26.61378 on 1 df, p=2.48465e-07, n=42
Wald test = 8.099306 on 1 df, p = 0.004428221
Covariance-Matrix:
ctDNA.SurveillancePOSITIVE
ctDNA.SurveillancePOSITIVE 2.375416cox_fit_summary <- summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.SurveillancePOSITIVE 4.386253 1.541238 80.33884 10.04137 10388.64 26.61378 2.48465e-07
Likelihood ratio test=26.61378 on 1 df, p=2.48465e-07, n=42
Wald test = 8.099306 on 1 df, p = 0.004428221
Covariance-Matrix:
ctDNA.SurveillancePOSITIVE
ctDNA.SurveillancePOSITIVE 2.375416circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 31.504, df = 1, p-value = 1.99e-08fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 2.243e-08
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
20.64708 Inf
sample estimates:
odds ratio
Inf print(contingency_table)
No Progression Progression
Negative 32 0
Positive 1 9table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at Surveillance Stage I/II",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#PFS by ctDNA status at surveillance Stage III/IV
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="III/IV",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 23 2 NA NA NA
ctDNA.Surveillance=POSITIVE 14 13 12 10.4 24.8event_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)# A tibble: 2 × 5
ctDNA.Surveillance Total Events Fraction Percentage
<chr> <int> <int> <dbl> <dbl>
1 NEGATIVE 23 2 0.0870 8.70
2 POSITIVE 14 13 0.929 92.9 surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance Stage III/IV", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 23 0 1.000 0.0000 1.000 1.000
12 18 2 0.913 0.0588 0.695 0.978
24 10 0 0.913 0.0588 0.695 0.978
36 4 0 0.913 0.0588 0.695 0.978
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 14 0 1.0000 0.0000 1.00000 1.000
12 7 7 0.5000 0.1336 0.22859 0.722
24 2 5 0.1429 0.0935 0.02322 0.366
36 1 1 0.0714 0.0688 0.00452 0.275circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 37, number of events= 15
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 2.7831 16.1695 0.7642 3.642 0.000271 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 16.17 0.06184 3.616 72.31
Concordance= 0.77 (se = 0.059 )
Likelihood ratio test= 21.57 on 1 df, p=3e-06
Wald test = 13.26 on 1 df, p=3e-04
Score (logrank) test = 23.62 on 1 df, p=1e-06cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 16.17 (3.62-72.31); p = 0"circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 22.2, df = 1, p-value = 2.457e-06fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 3.807e-07
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
9.097798 5733.101682
sample estimates:
odds ratio
101.4375 print(contingency_table)
No Progression Progression
Negative 21 2
Positive 1 13table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at Surveillance Stage III/IV",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#PFS by ctDNA status at surveillance p16(+)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Positive",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 32 0 NA NA NA
ctDNA.Surveillance=POSITIVE 10 9 10.7 3.12 NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)# A tibble: 2 × 5
ctDNA.Surveillance Total Events Fraction Percentage
<chr> <int> <int> <dbl> <dbl>
1 NEGATIVE 32 0 0 0
2 POSITIVE 10 9 0.9 90surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = TRUE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance p16(+)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 32 0 1 0 1 1
12 24 0 1 0 1 1
24 14 0 1 0 1 1
36 5 0 1 0 1 1
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 10 0 1.0 0.000 1.0000 1.000
12 5 5 0.5 0.158 0.1836 0.753
24 2 3 0.2 0.126 0.0309 0.475circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxphf(surv_object ~ ctDNA.Surveillance, data=circ_data)
summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.SurveillancePOSITIVE 4.506786 1.534426 90.63007 11.43341 11701.57 29.1584 6.669606e-08
Likelihood ratio test=29.1584 on 1 df, p=6.669606e-08, n=42
Wald test = 8.626644 on 1 df, p = 0.003312813
Covariance-Matrix:
ctDNA.SurveillancePOSITIVE
ctDNA.SurveillancePOSITIVE 2.354464cox_fit_summary <- summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.SurveillancePOSITIVE 4.506786 1.534426 90.63007 11.43341 11701.57 29.1584 6.669606e-08
Likelihood ratio test=29.1584 on 1 df, p=6.669606e-08, n=42
Wald test = 8.626644 on 1 df, p = 0.003312813
Covariance-Matrix:
ctDNA.SurveillancePOSITIVE
ctDNA.SurveillancePOSITIVE 2.354464circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 31.504, df = 1, p-value = 1.99e-08fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 2.243e-08
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
20.64708 Inf
sample estimates:
odds ratio
Inf print(contingency_table)
No Progression Progression
Negative 32 0
Positive 1 9table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at Surveillance p16(+)",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#PFS by ctDNA status at surveillance p16(-)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Negative",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 23 2 NA NA NA
ctDNA.Surveillance=POSITIVE 14 13 12 10.4 NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)# A tibble: 2 × 5
ctDNA.Surveillance Total Events Fraction Percentage
<chr> <int> <int> <dbl> <dbl>
1 NEGATIVE 23 2 0.0870 8.70
2 POSITIVE 14 13 0.929 92.9 surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance p16(-)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 23 0 1.000 0.0000 1.000 1.000
12 20 2 0.913 0.0588 0.695 0.978
24 11 0 0.913 0.0588 0.695 0.978
36 4 0 0.913 0.0588 0.695 0.978
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 14 0 1.000 0.0000 1.0000 1.000
12 7 7 0.500 0.1336 0.2286 0.722
24 3 4 0.214 0.1097 0.0521 0.448
36 2 1 0.143 0.0935 0.0232 0.366circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 37, number of events= 15
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 2.743 15.528 0.763 3.595 0.000325 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 15.53 0.0644 3.481 69.27
Concordance= 0.769 (se = 0.061 )
Likelihood ratio test= 21 on 1 df, p=5e-06
Wald test = 12.92 on 1 df, p=3e-04
Score (logrank) test = 22.81 on 1 df, p=2e-06cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 15.53 (3.48-69.27); p = 0"circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 22.2, df = 1, p-value = 2.457e-06fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 3.807e-07
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
9.097798 5733.101682
sample estimates:
odds ratio
101.4375 print(contingency_table)
No Progression Progression
Negative 21 2
Positive 1 13table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at Surveillance p16(-)",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#Multivariate cox regression for PFS ctDNA status at surveillance
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$cStage <- factor(circ_data$cStage, levels = c("I/II", "III/IV"))
circ_data$p16.status <- factor(circ_data$p16.status, levels = c("Negative", "Positive"))
circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Oropharynx", "Larynx/Hypopharynx", "Oral cavity", "Other (paranasal sinus and nasopharyngeal)"))
surv_object <- Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance + cStage + p16.status + Prim.Location, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for PFS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)#ctDNA and MTM/mL Dynamics for pts at surveillance window
#Dynamics and MTM/mL plots for patients with ctDNA negative at surveillance
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC ctDNA MTM.csv", stringsAsFactors = FALSE)
df <- df[df$ctDNA.Surveillance=="NEGATIVE",]
df$PFS.Event <- ifelse(df$PFS.Event %in% c("No", "no", "FALSE", "False", "0"), FALSE,
ifelse(df$PFS.Event %in% c("Yes", "yes", "TRUE", "True", "1"), TRUE, NA))
df$PFS.Event <- factor(df$PFS.Event, levels = c(FALSE, TRUE))
df <- df %>%
group_by(PatientName) %>%
filter(n() >= 2) %>% #keep only pts with at least 2 post-surgery time points
ungroup()
num_unique <- length(unique(df$PatientName))
cat("Number of unique patients:", num_unique, "\n")Number of unique patients: 55 df_patient_pfs <- df %>%
group_by(PatientName) %>%
dplyr::summarize(
PFS_True = any(PFS.Event == TRUE, na.rm = TRUE),
PFS_False = all(PFS.Event == FALSE, na.rm = TRUE)
)
num_true <- sum(df_patient_pfs$PFS_True)
num_false <- sum(df_patient_pfs$PFS_False)
cat("Number of unique patients with Event:", num_true, "\n")Number of unique patients with Event: 2 cat("Number of unique patients with No Event:", num_false, "\n")Number of unique patients with No Event: 53 p <- ggplot(df, aes(x = date.diff.months,
y = MTM.mL,
group = PatientName,
color = PFS.Event)) +
geom_line() + # Connect timepoints for each patient
geom_point() + # Add points for each timepoint
# Use a log10 scale for the y-axis with specified breaks
scale_y_log10(breaks = c(0.01, 0.1, 1, 10, 100, 1000),
labels = c("0.01","0.1", "1", "10", "100", "1000")) +
scale_x_continuous(breaks = seq(0, max(df$date.diff.months, na.rm = TRUE), by = 6)) +
scale_color_manual(values = c("FALSE" = "blue", "TRUE" = "red")) +
labs(
x = "Time Since Surgery or start of definitive treatment (months)",
y = "Mean Tumor Molecules per mL (MTM/mL)",
color = "PFS Event"
) +
theme_minimal()
print(p)
#Dynamics and MTM/mL plots for patients with ctDNA positive at surveillance
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC ctDNA MTM.csv", stringsAsFactors = FALSE)
df <- df[df$ctDNA.Surveillance=="POSITIVE",]
df$PFS.Event <- ifelse(df$PFS.Event %in% c("No", "no", "FALSE", "False", "0"), FALSE,
ifelse(df$PFS.Event %in% c("Yes", "yes", "TRUE", "True", "1"), TRUE, NA))
df$PFS.Event <- factor(df$PFS.Event, levels = c(FALSE, TRUE))
df <- df %>%
group_by(PatientName) %>%
filter(n() >= 2) %>% #keep only pts with at least 2 post-surgery time points
ungroup()
num_unique <- length(unique(df$PatientName))
cat("Number of unique patients:", num_unique, "\n")Number of unique patients: 23 df_patient_pfs <- df %>%
group_by(PatientName) %>%
dplyr::summarize(
PFS_True = any(PFS.Event == TRUE, na.rm = TRUE),
PFS_False = all(PFS.Event == FALSE, na.rm = TRUE)
)
num_true <- sum(df_patient_pfs$PFS_True)
num_false <- sum(df_patient_pfs$PFS_False)
cat("Number of unique patients with Event:", num_true, "\n")Number of unique patients with Event: 21 cat("Number of unique patients with No Event:", num_false, "\n")Number of unique patients with No Event: 2 p <- ggplot(df, aes(x = date.diff.months,
y = MTM.mL,
group = PatientName,
color = PFS.Event)) +
geom_line() + # Connect timepoints for each patient
geom_point() + # Add points for each timepoint
# Use a log10 scale for the y-axis with specified breaks
scale_y_log10(breaks = c(0.01, 0.1, 1, 10, 100, 1000, 10000),
labels = c("0.01","0.1", "1", "10", "100", "1000", "10000")) +
scale_x_continuous(breaks = seq(0, max(df$date.diff.months, na.rm = TRUE), by = 6)) +
scale_color_manual(values = c("FALSE" = "blue", "TRUE" = "red")) +
labs(
x = "Time Since Surgery or start of definitive treatment (months)",
y = "Mean Tumor Molecules per mL (MTM/mL)",
color = "PFS Event"
) +
theme_minimal()
print(p)
#PFS by ctDNA status at surveillance for pts with MRD & Surveillance time points available
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.complete=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 47 1 NA NA NA
ctDNA.Surveillance=POSITIVE 17 15 11.3 8.18 22.2event_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)# A tibble: 2 × 5
ctDNA.Surveillance Total Events Fraction Percentage
<chr> <int> <int> <dbl> <dbl>
1 NEGATIVE 47 1 0.0213 2.13
2 POSITIVE 17 15 0.882 88.2 surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 47 0 1.000 0.000 1.000 1.000
12 39 1 0.979 0.021 0.858 0.997
24 23 0 0.979 0.021 0.858 0.997
36 7 0 0.979 0.021 0.858 0.997
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 17 0 1.000 0.0000 1.0000 1.000
12 8 9 0.471 0.1211 0.2296 0.680
24 3 5 0.176 0.0925 0.0435 0.383
36 2 0 0.176 0.0925 0.0435 0.383circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 64, number of events= 16
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 4.141 62.867 1.035 4 6.34e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 62.87 0.01591 8.263 478.3
Concordance= 0.869 (se = 0.039 )
Likelihood ratio test= 41.58 on 1 df, p=1e-10
Wald test = 16 on 1 df, p=6e-05
Score (logrank) test = 54.15 on 1 df, p=2e-13cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 62.87 (8.26-478.32); p = 0"circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 44.883, df = 1, p-value = 2.092e-11fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 1.312e-11
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
24.1523 12883.6921
sample estimates:
odds ratio
254.5638 print(contingency_table)
No Progression Progression
Negative 46 1
Positive 2 15table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at Surveillance",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#Time-dependent analysis for PFS in longitudinal time points for pts with MRD & Surveillance time points available
rm(list=ls())
setwd("~/Downloads")
dt <- read_xlsx("CLIA HNSCC Peddada Clinical Data_Time dependent.xlsx") |>
clean_names() |>
mutate(across(.cols = c(window_start_date,dfs_date,
surveillance_1_date:surveillance_14_date),
.fns = ~ as_date(as.Date(.x, format = "%Y-%m-%d"))))
dt_biomarker <- dt |>
select(pts_id, ct_dna_complete,
window_start_date,
surveillance_1_status:surveillance_14_date) |>
filter(ct_dna_complete) |>
pivot_longer(cols = surveillance_1_status:surveillance_14_date,
names_to = c("visit_number", ".value"),
names_pattern = "surveillance_(.)_(.*)") |>
mutate(biomarker_time = day(days(date - window_start_date))) |>
select(pts_id, biomarker_time, biomarker_status = status) |>
filter(!is.na(biomarker_time))
glimpse(dt_biomarker)Rows: 285
Columns: 3
$ pts_id <chr> "UNM-002", "UNM-004", "UNM-004", "UNM-004", "UNM-004", "UNM-004", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-009", "UNM-0…
$ biomarker_time <dbl> 2, 17, 108, 197, 240, 269, 15, 55, 168, 244, 326, 412, 46, 136, 225, 313, 417, 508, 597, 35, 246, 112, 202, 293, 386, 477, 571, 3, 39, 82, 109, 195, …
$ biomarker_status <chr> "NEGATIVE", "NEGATIVE", "NEGATIVE", "POSITIVE", "POSITIVE", "POSITIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", NA, "NEGATIVE", "…dt_survival <- dt |>
select(pts_id, ct_dna_complete,
window_start_date:dfs_date, dfs_event) |> # Added dfs_event here
filter(ct_dna_complete) |>
mutate(dfs_time = (dfs_date - window_start_date),
dfs_time = day(days(dfs_time)),
dfs_event = as.numeric(dfs_event)) |>
select(pts_id, dfs_time, dfs_event)
glimpse(dt_survival)Rows: 64
Columns: 3
$ pts_id <chr> "UNM-002", "UNM-004", "UNM-008", "UNM-009", "UNM-014", "UNM-016", "UNM-019", "UNM-020", "UNM-022", "UNM-023", "UNM-024", "UNM-025", "UNM-026", "UNM-027", "U…
$ dfs_time <dbl> 977, 398, 1043, 603, 298, 745, 1155, 987, 1016, 237, 535, 970, 728, 934, 989, 437, 203, 647, 625, 48, 1033, 1159, 591, 70, 305, 373, 126, 918, 542, 282, 501…
$ dfs_event <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, …aux <- dt_survival %>%
filter(dfs_time <= 0)
tab <- left_join(aux, dt) |>
select(pts_id, window_start_date, dfs_time, dfs_date,
surveillance_1_date:surveillance_14_date) |>
mutate(across(.cols = dfs_date:surveillance_14_date,
.fns = ~ as_date(.x))) |>
select(pts_id, window_start_date, dfs_date, dfs_time)
datatable(tab, filter = "top")
dt_survival <- dt_survival |>
filter(dfs_time > 0)
aux <- dt |>
select(pts_id, ct_dna_complete,
window_start_date, dfs_date,
surveillance_1_date:surveillance_14_date) |>
mutate(across(.cols = surveillance_1_date:surveillance_14_date,
.fns = ~ .x - window_start_date)) |>
mutate(across(.cols = surveillance_1_date:surveillance_14_date,
.fns = ~ .x < 0)) |>
rowwise() |>
mutate(sum_neg =
sum(c_across(surveillance_1_date:surveillance_14_date),
na.rm = TRUE)) |>
select(pts_id, sum_neg)
tab <- left_join(aux, dt) |>
filter(sum_neg > 0) |>
select(pts_id, sum_neg, window_start_date,
surveillance_1_date:surveillance_14_date) |>
mutate(across(.cols = window_start_date:surveillance_14_date,
.fns = ~ as_date(.x)))
datatable(tab, filter = "top")
aux <- dt |>
select(pts_id, ct_dna_complete,
window_start_date, dfs_date,
surveillance_1_date:surveillance_14_date) |>
mutate(across(.cols = dfs_date:surveillance_14_date,
.fns = ~ .x - window_start_date)) |>
mutate(across(.cols = surveillance_2_date:surveillance_14_date,
.fns = ~ dfs_date < .x)) |>
rowwise() |>
mutate(n_biomarker_after_event = sum(c_across(surveillance_2_date:
surveillance_14_date),
na.rm = TRUE)) |>
mutate(across(.cols = surveillance_1_date:surveillance_14_date,
.fns = ~ !is.na(.x))) |>
mutate(total_biomarker = sum(c_across(surveillance_2_date:
surveillance_14_date),
na.rm = TRUE)) |>
select(pts_id, n_biomarker_after_event, total_biomarker)
temp <- aux |>
select(-pts_id) |>
group_by(n_biomarker_after_event, total_biomarker) |> # Direct grouping
summarise(freq = n(), .groups = "drop") # Drop groups after summarization
tab <- left_join(aux, dt) |>
select(pts_id, n_biomarker_after_event, total_biomarker,
dfs_date,
surveillance_2_date:surveillance_14_date) |>
mutate(across(.cols = dfs_date:surveillance_14_date,
.fns = ~ as_date(.x))) |>
filter(n_biomarker_after_event > 0)
datatable(tab, filter = "top")
aux <- tmerge(data1 = dt_survival,
data2 = dt_survival,
id = pts_id,
dfs_event = event(dfs_time, dfs_event))
dt_final <- tmerge(data1 = aux,
data2 = dt_biomarker,
id = pts_id,
biomarker_status =
tdc(biomarker_time, biomarker_status))
datatable(dt_final, filter = "top")
# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
# data = dt_final)
# summary(fit)
fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)
summary(fit)Call:
coxph(formula = Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)
n= 281, number of events= 16
(59 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
biomarker_statusPOSITIVE 4.991 147.056 1.040 4.801 1.58e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE 147.1 0.0068 19.17 1128
Concordance= 0.922 (se = 0.037 )
Likelihood ratio test= 60.06 on 1 df, p=9e-15
Wald test = 23.05 on 1 df, p=2e-06
Score (logrank) test = 125.2 on 1 df, p=<2e-16cox_fit_summary <- summary(fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 147.06 (19.17-1128.2); p = 0"#Median numbers of time points and lead time in the longitudinal setting for pts with MRD & Surveillance time points available
# Load the dataset
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.complete=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
median_Nsurvtps <- median(circ_datadf$Nsurvtps, na.rm = TRUE)
min_Nsurvtps <- min(circ_datadf$Nsurvtps, na.rm = TRUE)
max_Nsurvtps <- max(circ_datadf$Nsurvtps, na.rm = TRUE)
cat(sprintf("Median # of surveillance time points: %d (%d-%d)\n",
median_Nsurvtps, min_Nsurvtps, max_Nsurvtps))Median # of surveillance time points: 4 (1-20)#Median of median interval of ctDNA timepoints and radiological imaging assessment
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC UNM_OP.csv", stringsAsFactors = FALSE)
names(df) <- trimws(names(df))
# ---- Helper: compute median interval per patient ----
median_interval_per_patient <- function(data, filter_col, filter_val) {
data %>%
filter(!!sym(filter_col) == filter_val) %>%
arrange(PatientName, date.diff) %>%
group_by(PatientName) %>%
mutate(interval = date.diff - lag(date.diff)) %>%
summarise(median_interval = median(interval, na.rm = TRUE), .groups = "drop") %>%
mutate(event_group = filter_val)
}
# ---- Compute per-patient medians ----
ctDNA_patient_medians <- median_interval_per_patient(df, "Event", "ctDNA")
imaging_patient_medians <- median_interval_per_patient(df, "Event_type", "Imaging")
# ---- Function to summarize patient-level medians to cohort-level ----
cohort_summary <- function(patient_data, event_label) {
# Filter out patients with NA median intervals
valid_data <- patient_data %>% filter(!is.na(median_interval))
data.frame(
Event_Type = event_label,
Cohort_Median_Frequency = median(valid_data$median_interval, na.rm = TRUE),
Min_Patient_Median = min(valid_data$median_interval, na.rm = TRUE),
Max_Patient_Median = max(valid_data$median_interval, na.rm = TRUE),
Range_Patient_Median = max(valid_data$median_interval, na.rm = TRUE) -
min(valid_data$median_interval, na.rm = TRUE)
)
}
# ---- Combine all results ----
results <- bind_rows(
cohort_summary(ctDNA_patient_medians, "ctDNA"),
cohort_summary(imaging_patient_medians, "Imaging")
)
# ---- Print cohort-level summary ----
cat("===== Median Frequency (Days) per Cohort =====\n")===== Median Frequency (Days) per Cohort =====print(results, row.names = FALSE) Event_Type Cohort_Median_Frequency Min_Patient_Median Max_Patient_Median Range_Patient_Median
ctDNA 88.0 18 303 285
Imaging 246.5 22 1068 1046# ---- OPTIONAL: Save patient-level medians ----
patient_level_medians <- bind_rows(ctDNA_patient_medians, imaging_patient_medians)
#write.csv(patient_level_medians, "patient_median_intervals.csv", row.names = FALSE)#Plot for individual lead-time calculations for each pt
rm(list=ls())
csv_path <- "~/Downloads/CLIA HNSCC Peddada_Lead Time pts.csv"
df <- read.csv(csv_path, check.names = FALSE)
id_candidates <- c("Patient","ID","Pt","Subject","Sample")
id_col <- id_candidates[id_candidates %in% names(df)][1]
if (is.na(id_col)) {
df <- df %>% mutate(Patient = row_number())
id_col <- "Patient"
}
num_cols <- intersect(c("MR","CR","LT"), names(df))
df[num_cols] <- lapply(df[num_cols], function(x) suppressWarnings(as.numeric(x)))
# If LT missing, compute in days
if (!("LT" %in% names(df))) {
df <- df %>% mutate(LT = CR - MR)
}
# ---- convert to months ----
# Approximate: 30.44 days per month (average)
days_to_months <- function(x) x / 30.437
df <- df %>%
mutate(MR_mo = days_to_months(MR),
CR_mo = days_to_months(CR),
LT_mo = days_to_months(LT))
# ---- ordering for y-axis ----
df <- df %>%
mutate(Earliest = pmin(MR_mo, CR_mo, na.rm = TRUE)) %>%
arrange(Earliest) %>%
mutate(Patient_f = factor(.data[[id_col]], levels = .data[[id_col]]))
# ---- long format for points ----
points_long <- df %>%
select(Patient_f, MR_mo, CR_mo) %>%
pivot_longer(c(MR_mo, CR_mo), names_to = "Type", values_to = "Months") %>%
mutate(Type = dplyr::recode(Type,
"MR_mo" = "Molecular recurrence",
"CR_mo" = "Clinical recurrence"))
# ---- segments between MR and CR ----
segments_df <- df %>%
transmute(Patient_f,
x0 = MR_mo, x1 = CR_mo,
lt_flag = if_else(LT > 120, "gt120", "le120"))
# ---- annotation ----
med_lt <- median(df$LT_mo, na.rm = TRUE)
min_lt <- min(df$LT_mo, na.rm = TRUE)
max_lt <- max(df$LT_mo, na.rm = TRUE)
annot_label <- sprintf("Median lead-time:\n%.1f months (%.1f to %.1f)",
med_lt, min_lt, max_lt)
# ---- plot ----
pal <- c("Molecular recurrence" = "#10B4C1",
"Clinical recurrence" = "#C96A72")
x_max <- max(c(df$MR_mo, df$CR_mo), na.rm = TRUE)
y_mid <- levels(df$Patient_f)[ceiling(nlevels(df$Patient_f) * 0.55)]
p <- ggplot() +
geom_segment(data = segments_df,
aes(x = x0, xend = x1, y = Patient_f, yend = Patient_f,
linetype = lt_flag),
linewidth = 0.6, color = "black") +
scale_linetype_manual(values = c(le120 = "solid", gt120 = "dashed"),
guide = "none") +
geom_point(data = points_long,
aes(x = Months, y = Patient_f, color = Type),
size = 2.8) +
scale_color_manual(values = pal, name = NULL) +
labs(x = "Months from Surgery or Definitive Treatment", y = NULL) +
annotate("text",
x = x_max * 0.73,
y = y_mid,
label = annot_label,
hjust = 0, vjust = 0.5, size = 3.8) +
scale_x_continuous(breaks = seq(0, ceiling(x_max/3)*3, by = 3)) +
coord_cartesian(xlim = c(0, x_max * 1.05)) +
theme_bw(base_size = 12) +
theme(
panel.grid.major = element_line(color = "grey85", linewidth = 0.3),
panel.grid.minor = element_line(color = "grey92", linewidth = 0.2),
axis.text.y = element_text(size = 9)
)
print(p)
wc_df <- df %>% dplyr::select(MR, CR) %>% tidyr::drop_na()
paired_diff <- wc_df$CR - wc_df$MR
wilx <- wilcox.test(
x = wc_df$CR, y = wc_df$MR,
paired = TRUE,
alternative = "two.sided",
conf.int = TRUE, # gives CI for the Hodges–Lehmann estimate of the median difference
exact = FALSE # safer for ties/large N
)
W_stat <- unname(wilx$statistic) # Wilcoxon V
p_value <- wilx$p.value
HL_est <- unname(wilx$estimate) # median of (CR - MR)
HL_ci <- wilx$conf.int # CI for median difference
# Simple p-value formatter for annotation/publication
fmt_p <- function(p) {
if (is.na(p)) return("NA")
if (p < 0.001) "< 0.001" else sprintf("= %.3f", round(p, 3))
}
p_text <- paste0("P ", fmt_p(p_value)) # e.g., "P = 0.003" or "P < 0.001"
# Optional: print a compact summary
cat("\nPaired Wilcoxon signed-rank test (CR vs MR)\n",
"-------------------------------------------\n",
sprintf("N pairs: %d", nrow(wc_df)), "\n",
sprintf("W (V): %g", W_stat), "\n",
sprintf("P-value: %s", ifelse(p_value < 0.001, "< 0.001", sprintf("%.6f", p_value))), "\n",
sprintf("Hodges–Lehmann median difference (CR - MR): %.1f days", HL_est), "\n",
sprintf("95%% CI: [%.1f, %.1f] days", HL_ci[1], HL_ci[2]), "\n", sep = "")
Paired Wilcoxon signed-rank test (CR vs MR)
-------------------------------------------
N pairs: 22
W (V): 253
P-value: < 0.001
Hodges–Lehmann median difference (CR - MR): 145.5 days
95% CI: [86.5, 248.5] days#ctDNA velocity and lead time liner regression
csv_path <- "~/Downloads/CLIA HNSCC Peddada_ctDNA velocity.csv" # <- adjust if needed
df_raw <- read.csv(csv_path, check.names = FALSE)
df <- df_raw %>%
rename(MTM_mL = `MTM/mL`) %>%
mutate(
daysCR.months = as.numeric(daysCR.months),
MTM_mL = as.numeric(MTM_mL)
) %>%
filter(!is.na(PatientName), !is.na(daysCR.months), !is.na(MTM_mL))
preCR <- df %>%
filter(daysCR.months <= 0, is.finite(MTM_mL), MTM_mL > 0)
eligible <- preCR %>%
group_by(PatientName) %>%
filter(n() >= 2, n_distinct(daysCR.months) >= 2) %>%
ungroup()
if (nrow(eligible) == 0) {
warning("No patients have ≥2 valid pre-recurrence points with distinct times; regression lines will be omitted.")
}
fits <- eligible %>%
group_by(PatientName) %>%
summarise(x_min = min(daysCR.months, na.rm = TRUE), .groups = "drop") %>%
mutate(grid = map(x_min, ~seq(.x, 0, length.out = 50))) %>%
select(PatientName, grid)
predict_patient <- function(dat, newx) {
if (length(newx) == 0) {
return(tibble(daysCR.months = numeric(0), MTM_mL = numeric(0)))
}
dat2 <- dat %>%
filter(is.finite(MTM_mL), MTM_mL > 0) %>%
mutate(log_ctdna = log10(MTM_mL))
if (nrow(dat2) < 2 || n_distinct(dat2$daysCR.months) < 2 || any(!is.finite(dat2$log_ctdna))) {
return(tibble(daysCR.months = numeric(0), MTM_mL = numeric(0)))
}
m <- lm(log_ctdna ~ daysCR.months, data = dat2)
tibble(
daysCR.months = newx,
MTM_mL = 10 ^ predict(m, newdata = tibble(daysCR.months = newx))
)
}
pred_lines <- eligible %>%
group_by(PatientName) %>%
tidyr::nest() %>% # list-column "data" per patient
left_join(fits, by = "PatientName") %>% # list-column "grid" per patient
mutate(pred = map2(data, grid, ~predict_patient(.x, .y))) %>%
select(PatientName, pred) %>%
tidyr::unnest(pred)
pooled_line <- {
if (nrow(preCR) >= 2 && n_distinct(preCR$daysCR.months) >= 2) {
pooled_x <- seq(min(preCR$daysCR.months, na.rm = TRUE), 0, length.out = 100)
pooled_fit <- lm(log10(MTM_mL) ~ daysCR.months, data = preCR)
tibble(
daysCR.months = pooled_x,
MTM_mL = 10 ^ predict(pooled_fit, newdata = tibble(daysCR.months = pooled_x))
)
} else {
tibble(daysCR.months = numeric(0), MTM_mL = numeric(0))
}
}
x_min <- floor(min(df$daysCR.months, na.rm = TRUE) / 3) * 3
x_max <- ceiling(max(df$daysCR.months, na.rm = TRUE) / 3) * 3
y_min_pos <- max(min(df$MTM_mL[df$MTM_mL > 0], na.rm = TRUE) / 2, 0.01)
y_max_pos <- 10 ^ ceiling(log10(max(df$MTM_mL, na.rm = TRUE)))
log_breaks <- 10 ^ seq(floor(log10(y_min_pos)), ceiling(log10(y_max_pos)))
p <- ggplot() +
# per-patient fitted lines (if any)
geom_line(data = pred_lines,
aes(x = daysCR.months, y = MTM_mL, color = PatientName),
linewidth = 1) +
# pooled dashed trend (if any)
geom_line(data = pooled_line,
aes(x = daysCR.months, y = MTM_mL),
linewidth = 0.8, linetype = "dashed", color = "grey35") +
# raw points (all timepoints, before and after)
geom_point(data = df,
aes(x = daysCR.months, y = MTM_mL, color = PatientName),
size = 1.8, alpha = 0.85) +
# vertical line at imaging-positive date
geom_vline(xintercept = 0, linetype = "dashed", linewidth = 0.8, color = "black") +
scale_y_log10(breaks = log_breaks,
labels = label_number(accuracy = 1)) +
scale_x_continuous(breaks = seq(x_min, x_max, by = 3)) + # every 3 months
labs(
x = "Months before/after clinical recurrence (0 = imaging positive)",
y = "ctDNA level (MTM/mL)",
color = "Patient"
) +
theme_bw(base_size = 12) +
theme(
panel.grid.major = element_line(color = "grey85", linewidth = 0.3),
panel.grid.minor = element_line(color = "grey92", linewidth = 0.2),
legend.position = "none" # change to "right" if you want the legend
)
print(p)
#Patient Specific Plot for UNM-069
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC_Pt Specific Plot data.csv")
library(ggplot2)
library(dplyr)
library(scales)
# 1. FILTER FOR PATIENT
target_patient <- "UNM-069"
plot_data <- circ_data %>% filter(PatientName == target_patient)
# 2. Process Data
# We fix the pseudo-zero for log scale but allow high values (like 18.08) to remain
data_Signatera <- plot_data %>%
filter(Event_type %in% c("ctDNA_pos", "ctDNA_neg")) %>%
mutate(MTM_Log = ifelse(MTM.mL == 0, 0.001, MTM.mL))
data_Imaging_NED <- plot_data %>% filter(Event_type == "Imaging" & Event == "NED")
data_Imaging_PD <- plot_data %>% filter(Event_type == "Imaging" & Event == "PD")
data_Biopsy <- plot_data %>% filter(Event_type == "Biopsy" | Event == "Biopsy")
data_Treatment <- plot_data %>%
filter(!is.na(Tx_type) & Tx_type != "NA" & Tx_type != "" & !is.na(Tx_start.months)) %>%
distinct(Tx_type, Tx_start.months, Tx_end.months)
# 3. Create the Plot
ggplot() +
# Treatment Rectangles - Expanded ymax to match new scale
geom_rect(data = data_Treatment,
aes(xmin = Tx_start.months, xmax = Tx_end.months, ymin = 0.004, ymax = 100),
fill = "purple", alpha = 0.15) +
geom_text(data = data_Treatment,
aes(x = (Tx_start.months + Tx_end.months)/2, y = 50, label = Tx_type),
angle = 90, color = "purple", size = 3, fontface = "bold") +
# ctDNA Line - Now it will connect because the limits are higher
geom_line(data = data_Signatera, aes(x = date.diff.months, y = MTM_Log),
color = "black", linewidth = 0.7) +
# ctDNA Points
geom_point(data = data_Signatera,
aes(x = date.diff.months, y = MTM_Log, fill = Event_type),
shape = 21, size = 3.5, color = "black") +
# Clinical Events
geom_point(data = data_Imaging_NED, aes(x = date.diff.months, y = 0.005, color = "NED in Scan"),
shape = 25, size = 4, fill = "green") +
geom_point(data = data_Imaging_PD, aes(x = date.diff.months, y = 0.005, color = "PD (Progression)"),
shape = 25, size = 4, fill = "red") +
geom_point(data = data_Biopsy, aes(x = date.diff.months, y = 0.005, color = "Biopsy"),
shape = 23, size = 4, fill = "orange") +
# X-Axis: 3-month intervals
scale_x_continuous(breaks = seq(0, max(plot_data$date.diff.months, na.rm=T) + 6, by = 3)) +
# Y-Axis: EXPANDED Log10 scale to include 18.08
scale_y_log10(breaks = c(0.001, 0.01, 0.1, 1, 10, 100),
labels = c("0", "0.01", "0.1", "1", "10", "100"),
limits = c(0.001, 100)) +
scale_fill_manual(values = c("ctDNA_pos" = "black", "ctDNA_neg" = "white"),
labels = c("Negative", "Positive"), name = "ctDNA Status") +
scale_color_manual(values = c("NED in Scan" = "darkgreen",
"PD (Progression)" = "darkred",
"Biopsy" = "darkorange"),
name = "Clinical Events") +
guides(color = guide_legend(override.aes = list(shape = c(23, 25, 25),
fill = c("orange", "green", "red")))) +
labs(x = "Months from Diagnosis/Surgery",
y = "MTM/mL",
title = paste("Clinical Timeline:", target_patient)) +
theme_minimal() +
theme(panel.grid.minor = element_blank())
---
title: "HNSCC UNM Althoff et al_Clinical Analysis"
output: html_notebook
---

library(swimplot)
library(grid)
library(gtable)
library(readr) 
library(mosaic)
library(forcats)
library(dplyr) 
library(survival) 
library(survminer) 
library(ggplot2)
library(scales)
library(coxphf)
library(ggthemes)
library(tidyverse)
library(gtsummary)
library(flextable)
library(reshape2)
library(parameters)
library(car)
library(ComplexHeatmap)
library(tidyverse)
library(readxl)
library(janitor)
library(DT)
library(pROC)
library(rms)

#ctDNA Detection Rates by Window and Stages
```{r}
#ctDNA at Baseline
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.Base %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Base == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Base, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Base == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA at MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA at Surveillance
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
```



#Demographics Table
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

circ_data_subset <- circ_data %>%
  select(
    Sex,
    Age,
    Tobacco.History,
    Prim.Location,
    cT,
    cN,
    cM,
    Histology,
    Stage,
    p16.status,
    Treatment.Group,
    PFS.Event,
    OS.Event,
    OS.months) %>%
  mutate(
    Sex = factor(Sex),
    Age = as.numeric(Age),
    Tobacco.History = factor(Tobacco.History),
    Prim.Location = factor(Prim.Location),
    cT = factor(cT),
    cN = factor(cN),
    cM = factor(cM),
    Histology = factor(Histology),
    Stage = factor(Stage),
    p16.status = factor(p16.status),
    Treatment.Group = factor(Treatment.Group),
    PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
    OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
    OS.months = as.numeric(OS.months)) 
table1 <- circ_data_subset %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
table1
fit1 <- as_flex_table(
  table1,
  include = everything(),
  return_calls = FALSE
)
fit1
save_as_docx(fit1, path= "~/Downloads/1. CLIA HNSCC UNM Demographics Table.docx")
```



#Demographics Table by ctDNA at baseline
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

circ_data_subset1 <- circ_data %>%
  select(
    Sex,
    Age,
    Tobacco.History,
    Prim.Location,
    cT,
    cN,
    cM,
    Histology,
    Stage,
    p16.status,
    Treatment.Group,
    PFS.Event,
    OS.Event,
    OS.months) %>%
  mutate(
    Sex = factor(Sex),
    Age = as.numeric(Age),
    Tobacco.History = factor(Tobacco.History),
    Prim.Location = factor(Prim.Location),
    cT = factor(cT),
    cN = factor(cN),
    cM = factor(cM),
    Histology = factor(Histology),
    Stage = factor(Stage),
    p16.status = factor(p16.status),
    Treatment.Group = factor(Treatment.Group),
    PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
    OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
    OS.months = as.numeric(OS.months)) 

circ_data1 <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

circ_data_subset2 <- circ_data1 %>%
  select(
    Sex,
    Age,
    Tobacco.History,
    Prim.Location,
    cT,
    cN,
    cM,
    Histology,
    Stage,
    p16.status,
    Treatment.Group,
    PFS.Event,
    OS.Event,
    OS.months,
    ctDNA.Base) %>%
  mutate(
    Sex = factor(Sex),
    Age = as.numeric(Age),
    Tobacco.History = factor(Tobacco.History),
    Prim.Location = factor(Prim.Location),
    cT = factor(cT),
    cN = factor(cN),
    cM = factor(cM),
    Histology = factor(Histology),
    Stage = factor(Stage),
    p16.status = factor(p16.status),
    Treatment.Group = factor(Treatment.Group),
    PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
    OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
    OS.months = as.numeric(OS.months),
    ctDNA.Base = factor(ctDNA.Base, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive")))
Overall <- circ_data_subset1 %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
Overall

ByctDNA_MRD <- circ_data_subset2 %>%
  tbl_summary(
    by = ctDNA.Base, # add this line to subgroup by ctDNA.Base
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels()
ByctDNA_MRD

merged_table <- tbl_merge(tbls=list(Overall, ByctDNA_MRD))
merged_table

fit1 <- as_flex_table(
  merged_table,
  include = everything(),
  return_calls = FALSE
)
fit1
save_as_docx(fit1, path = "~/Downloads/1b. CLIA HNSCC UNM Demographics Table by ctDNA.docx")
```

#Overview plot by Stage
```{r}
setwd("~/Downloads") 
clinstage <- read.csv("CLIA HNSCC UNM_OP.csv")
clinstage_df <- as.data.frame(clinstage)

# Creating the basic swimmer plot
oplot <- swimmer_plot(df=clinstage_df,
                      id='PatientName',
                      end='fu.diff.months',
                      fill='gray',
                      width=.01,
                      base_size = 14,
                      stratify= c('Stage'))

# Adding themes and scales
oplot <- oplot + theme(panel.border = element_blank())
oplot <- oplot + scale_y_continuous(breaks = seq(0, 72, by = 3))
oplot <- oplot + labs(x ="Patients", y="Months from Diagnosis")

# Adding swimmer points
oplot_ev1 <- oplot + swimmer_points(df_points=clinstage_df,
                                    id='PatientName',
                                    time='date.diff.months',
                                    name_shape ='Event_type',
                                    name_col = 'Event',
                                    size=3.5,fill='black')
# Optionally uncomment and use col='darkgreen' if needed

# Adding shape manual scale
oplot_ev1.1 <- oplot_ev1 + ggplot2::scale_shape_manual(name="Event_type",
                                                       values=c(1,16,6,18,18,4),
                                                       breaks=c('ctDNA_neg','ctDNA_pos', 'Imaging','Surgery','Biopsy', 'Death'))

# Display the plot
oplot_ev1.1
oplot_ev2 <- oplot_ev1.1 + swimmer_lines(df_lines=clinstage_df,
                                         id='PatientName',
                                         start='Tx_start.months',
                                         end='Tx_end.months',
                                         name_col='Tx_type',
                                         size=3.5,
                                         name_alpha = 1.0)
oplot_ev2 <- oplot_ev2 + guides(linetype = guide_legend(override.aes = list(size = 5, color = "black")))
oplot_ev2
oplot_ev2.2 <- oplot_ev2 + ggplot2::scale_color_manual(name="Event",values=c( "grey", "orange", "black", "black", "green", "red", "purple", "blue"))
oplot_ev2.2
```

#PFS in Complete Cohort (N=97)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.available, data = circ_data)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.available, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue"), title="PFS - Complete Cohort (n=97)", ylab= "Progression-Free Survival", xlab="Months from Start of definitive Treatment", legend.labs=c("Complete cohort"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))
```

#OS in Complete Cohort (N=97)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.available, data = circ_data)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.available, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue"), title="OS - Complete Cohort (n=97)", ylab= "Overall Survival", xlab="Months from Start of definitive Treatment", legend.labs=c("Complete cohort"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))
```

#Association of Baseline ctDNA MTM levels with clinicopathological factors
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cStage, data=circ_data, margins = TRUE)
circ_data$cStage <- factor(circ_data$cStage, levels = c("I/II","III/IV"), labels = c("I/II (n=34)","III/IV (n=29)"))
boxplot(ctDNA.Base.MTM~cStage, data=circ_data, main="ctDNA pre-treatment MTM - Stage", xlab="Stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.Stage <- circ_data %>%
  group_by(cStage) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.Stage)
m1<-wilcox.test(ctDNA.Base.MTM ~ cStage, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m1)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cT.status, data=circ_data, margins = TRUE)
circ_data$cT.status <- factor(circ_data$cT.status, levels = c("T0-T2","T3-T4"), labels = c("T0-T2 (n=29)","T3-T4 (n=34)"))
boxplot(ctDNA.Base.MTM~cT.status, data=circ_data, main="ctDNA pre-treatment MTM - T stage", xlab="T stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.cT <- circ_data %>%
  group_by(cT.status) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cT)
m2<-wilcox.test(ctDNA.Base.MTM ~ cT.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m2)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cT, data=circ_data, margins = TRUE)
circ_data$cT <- factor(circ_data$cT, levels = c("T0","T1","T2","T3","T4"))
boxplot(ctDNA.Base.MTM~cT, data=circ_data, main="ctDNA pre-treatment MTM - cT status", xlab="cT status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.cT <- circ_data %>%
  group_by(cT) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cT)
pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$cT, 
                                        p.adjust.method = "none", 
                                        exact = FALSE)
print(pairwise_wilcox)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cT <- factor(circ_data$cT, levels = c("T0", "T1", "T2", "T3", "T4"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
cT_levels <- levels(circ_data$cT)
p_value_matrix <- matrix(NA, nrow = length(cT_levels), ncol = length(cT_levels))
rownames(p_value_matrix) <- cT_levels
colnames(p_value_matrix) <- cT_levels

for (i in 1:length(cT_levels)) {
  for (j in i:length(cT_levels)) {
    if (i != j) {
      # Extract data for both groups
      data1 <- circ_data %>% filter(cT == cT_levels[i]) %>% pull(ctDNA.Base.MTM)
      data2 <- circ_data %>% filter(cT == cT_levels[j]) %>% pull(ctDNA.Base.MTM)
      
      # Perform Wilcoxon test and store p-value
      test_result <- wilcox.test(data1, data2, exact = FALSE)
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Make symmetric
    } else {
      p_value_matrix[i, j] <- 1  # Self-comparison = 1
    }
  }
}

p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("cT1", "cT2", "p_value")
p_value_data <- p_value_data %>%
  mutate(
    significance = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

ggplot(p_value_data, aes(x = cT1, y = cT2, fill = p_value)) +
  geom_tile(color = "white", size = 0.8) +  # Thicker grid lines for separation
  geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") +  # Significance markers
  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) +  # Gradient colors
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        panel.grid = element_blank()) +
  labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by cT)",
       x = "cT Status", y = "cT Status", fill = "P-Value")

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cN.status, data=circ_data, margins = TRUE)
circ_data$cN.status <- factor(circ_data$cN.status, levels = c("N0","N1-N3"), labels = c("N0 (n=12)","N1-N3 (n=51)"))
boxplot(ctDNA.Base.MTM~cN.status, data=circ_data, main="ctDNA pre-treatment MTM - cN status", xlab="cN status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.cN <- circ_data %>%
  group_by(cN.status) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cN)
m3<-wilcox.test(ctDNA.Base.MTM ~ cN.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m3)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cN, data=circ_data, margins = TRUE)
circ_data$cN <- factor(circ_data$cN, levels = c("N0","N1","N2","N3"))
boxplot(ctDNA.Base.MTM~cN, data=circ_data, main="ctDNA pre-treatment MTM - N Stage", xlab="N Stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 500))
median_ctDNA.cN <- circ_data %>%
  group_by(cN) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cN)
pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$cN, 
                                        p.adjust.method = "none", 
                                        exact = FALSE)
print(pairwise_wilcox)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cN <- factor(circ_data$cN, levels = c("N0","N1","N2","N3"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
cN_levels <- levels(circ_data$cN)
p_value_matrix <- matrix(NA, nrow = length(cN_levels), ncol = length(cN_levels))
rownames(p_value_matrix) <- cN_levels
colnames(p_value_matrix) <- cN_levels

for (i in 1:length(cN_levels)) {
  for (j in i:length(cN_levels)) {
    if (i != j) {
      # Extract data for both groups
      data1 <- circ_data %>% filter(cN == cN_levels[i]) %>% pull(ctDNA.Base.MTM)
      data2 <- circ_data %>% filter(cN == cN_levels[j]) %>% pull(ctDNA.Base.MTM)
      
      # Perform Wilcoxon test and store p-value
      test_result <- wilcox.test(data1, data2, exact = FALSE)
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Make symmetric
    } else {
      p_value_matrix[i, j] <- 1  # Self-comparison = 1
    }
  }
}

p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("cN1", "cN2", "p_value")
p_value_data <- p_value_data %>%
  mutate(
    significance = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

ggplot(p_value_data, aes(x = cN1, y = cN2, fill = p_value)) +
  geom_tile(color = "white", size = 0.8) +  # Thicker grid lines for separation
  geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") +  # Significance markers
  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) +  # Gradient colors
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        panel.grid = element_blank()) +
  labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by cN)",
       x = "Status", y = "cN Status", fill = "P-Value")

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cT <- factor(circ_data$cT, levels = c("T0", "T1", "T2", "T3", "T4"))
circ_data$cN <- factor(circ_data$cN, levels = c("N0", "N1", "N2", "N3"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)

median_ctDNA <- circ_data %>%
  group_by(cT, cN) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE)) %>%
  ungroup()

p_value_matrix <- dcast(median_ctDNA, cT ~ cN, value.var = "median_ctDNA_Base_MTM")
p_value_data <- melt(p_value_matrix, id.vars = "cT", variable.name = "cN", value.name = "median_value")
p_value_data$missing <- ifelse(is.na(p_value_data$median_value), "Missing", "Present")
p_value_data$median_value[is.na(p_value_data$median_value)] <- 0

ggplot(p_value_data, aes(x = cN, y = cT, fill = median_value)) +
  geom_tile(color = "black", size = 0.5) +  # Black gridlines for separation
  geom_text(aes(label = round(median_value, 2)), color = "black", size = 5) +  # Display median values
  scale_fill_gradient(low = "white", high = "blue") +  # Color gradient similar to the reference image
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        panel.grid = element_blank()) +
  labs(title = "Median ctDNA.Base.MTM by cT and cN",
       x = "cN Status", y = "cT Status", fill = "Median MTM") +
  geom_tile(data = subset(p_value_data, missing == "Missing"), 
            aes(x = cN, y = cT), color = "black", fill = NA, size = 0.5, linetype = "dashed")  # Add diagonal cross for missing values

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cM, data=circ_data, margins = TRUE)
circ_data$cM <- factor(circ_data$cM, levels = c("M0","M1"), labels = c("M0 (n=61)","M1 (n=2)"))
boxplot(ctDNA.Base.MTM~cM, data=circ_data, main="ctDNA pre-treatment MTM - cM", xlab="cM", ylab="MTM/mL", col="white",border="black", ylim = c(0, 500))
median_ctDNA.cM <- circ_data %>%
  group_by(cM) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cM)
m4<-wilcox.test(ctDNA.Base.MTM ~ cM, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m4)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~p16.status, data=circ_data, margins = TRUE)
circ_data$p16.status <- factor(circ_data$p16.status, levels = c("Negative","Positive"), labels = c("p16 neg (n=24)","p16 pos (n=39)"))
boxplot(ctDNA.Base.MTM~p16.status, data=circ_data, main="ctDNA pre-treatment MTM - p16 status", xlab="p16 status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.p16 <- circ_data %>%
  group_by(p16.status) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.p16)
m5<-wilcox.test(ctDNA.Base.MTM ~ p16.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m5)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~Prim.Location, data=circ_data, margins = TRUE)
circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Larynx/Hypopharynx","Oral cavity", "Oropharynx", "Other (paranasal sinus and nasopharyngeal)"))
boxplot(ctDNA.Base.MTM~Prim.Location, data=circ_data, main="ctDNA pre-treatment MTM - Tumor Location", xlab="Tumor Location", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.loc <- circ_data %>%
  group_by(Prim.Location) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.loc)
pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$Prim.Location, 
                                        p.adjust.method = "none", 
                                        exact = FALSE)

print(pairwise_wilcox)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Larynx/Hypopharynx","Oral cavity", "Oropharynx", "Other (paranasal sinus and nasopharyngeal)"), labels = c("LRX/HPRX","OC","PRX","Other"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
pl_levels <- levels(circ_data$Prim.Location)
p_value_matrix <- matrix(NA, nrow = length(pl_levels), ncol = length(pl_levels))
rownames(p_value_matrix) <- pl_levels
colnames(p_value_matrix) <- pl_levels

for (i in 1:length(pl_levels)) {
  for (j in i:length(pl_levels)) {
    if (i != j) {
      # Extract data for both groups
      data1 <- circ_data %>% filter(Prim.Location == pl_levels[i]) %>% pull(ctDNA.Base.MTM)
      data2 <- circ_data %>% filter(Prim.Location == pl_levels[j]) %>% pull(ctDNA.Base.MTM)
      
      # Perform Wilcoxon test and store p-value
      test_result <- wilcox.test(data1, data2, exact = FALSE)
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Make symmetric
    } else {
      p_value_matrix[i, j] <- 1  # Self-comparison = 1
    }
  }
}

p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("pl1", "pl2", "p_value")
p_value_data <- p_value_data %>%
  mutate(
    significance = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

ggplot(p_value_data, aes(x = pl1, y = pl2, fill = p_value)) +
  geom_tile(color = "white", size = 0.8) +  # Thicker grid lines for separation
  geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") +  # Significance markers
  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) +  # Gradient colors
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        panel.grid = element_blank()) +
  labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by Tumor Location)",
       x = "Tumor Location", y = "Tumor Location", fill = "P-Value")
```

#PFS by ctDNA status at MRD
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#OS by ctDNA status at MRD
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA at MRD", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Living Status",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at MRD - exclude pts with adjuvant treatment post-MRD
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
excluded_ids <- c("UNM-007", "UNM-008", "UNM-023", "UNM-027", "UNM-029", 
                  "UNM-030", "UNM-035", "UNM-045", "UNM-051", "UNM-059", 
                  "UNM-075", "UNM-082", "UNM-032", "UNM-042", "UNM-043", 
                  "UNM-048", "UNM-050", "UNM-070")
circ_data <- circ_data[!circ_data$PatientID %in% excluded_ids, ]

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#OS by ctDNA status at MRD - exclude pts with adjuvant treatment post-MRD
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
excluded_ids <- c("UNM-007", "UNM-008", "UNM-023", "UNM-027", "UNM-029", 
                  "UNM-030", "UNM-035", "UNM-045", "UNM-051", "UNM-059", 
                  "UNM-075", "UNM-082", "UNM-032", "UNM-042", "UNM-043", 
                  "UNM-048", "UNM-050", "UNM-070")
circ_data <- circ_data[!circ_data$PatientID %in% excluded_ids, ]

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA at MRD", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Living Status",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at MRD Stage I/II
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="I/II",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD Stage I/II", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD Stage I/II", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at MRD Stage III/IV
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="III/IV",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD Stage III/IV", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD Stage I/II", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA at MRD p16(+)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Positive",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD p16(+)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD p16(+)", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA at MRD p16(-)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Negative",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD p16(-)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD p16(-)", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at surveillance
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#OS by ctDNA status at surveillance
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA at Surveillance", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#Median numbers of time points in the longitudinal setting
```{r}
# Load the dataset
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

median_Nsurvtps <- median(circ_datadf$Nsurvtps, na.rm = TRUE)
min_Nsurvtps <- min(circ_datadf$Nsurvtps, na.rm = TRUE)
max_Nsurvtps <- max(circ_datadf$Nsurvtps, na.rm = TRUE)

cat(sprintf("Median # of surveillance time points: %d (%d-%d)\n", 
            median_Nsurvtps, min_Nsurvtps, max_Nsurvtps))
```

#Time-dependent analysis for PFS in longitudinal time points
```{r}
rm(list=ls())
setwd("~/Downloads")
dt <- read_xlsx("CLIA HNSCC Peddada Clinical Data_Time dependent.xlsx") |>
  clean_names() |>
  mutate(across(.cols = c(window_start_date,dfs_date,
                          surveillance_1_date:surveillance_13_date), 
                .fns = ~ as_date(as.Date(.x, format = "%Y-%m-%d"))))

dt_biomarker <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date,
         surveillance_1_status:surveillance_13_date) |>
  filter(ct_dna_surveillance_available) |>
  pivot_longer(cols = surveillance_1_status:surveillance_13_date,
               names_to = c("visit_number", ".value"),
               names_pattern = "surveillance_(.)_(.*)") |>
  mutate(biomarker_time = day(days(date - window_start_date))) |>
  select(pts_id, biomarker_time, biomarker_status = status) |>
  filter(!is.na(biomarker_time))

glimpse(dt_biomarker)

dt_survival <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date:dfs_date, dfs_event) |>  # Added dfs_event here
  filter(ct_dna_surveillance_available) |>
  mutate(dfs_time = (dfs_date - window_start_date),
         dfs_time = day(days(dfs_time)),
         dfs_event = as.numeric(dfs_event)) |>
  select(pts_id, dfs_time, dfs_event)

glimpse(dt_survival)

aux <- dt_survival %>% 
  filter(dfs_time <= 0)

tab <- left_join(aux, dt) |>
  select(pts_id, window_start_date, dfs_time, dfs_date,
         surveillance_1_date:surveillance_14_date) |>
  mutate(across(.cols = dfs_date:surveillance_14_date, 
                .fns = ~ as_date(.x))) |>
  select(pts_id, window_start_date, dfs_date, dfs_time)

datatable(tab, filter = "top")

dt_survival <- dt_survival |>
  filter(dfs_time > 0)

aux <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_14_date) |>
  mutate(across(.cols = surveillance_1_date:surveillance_14_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_14_date, 
                .fns = ~ .x < 0)) |>
  rowwise() |>
  mutate(sum_neg = 
           sum(c_across(surveillance_1_date:surveillance_14_date),
               na.rm = TRUE))  |>
  select(pts_id, sum_neg)

tab <- left_join(aux, dt) |>
  filter(sum_neg > 0) |>
  select(pts_id, sum_neg, window_start_date,
         surveillance_1_date:surveillance_14_date) |>
  mutate(across(.cols = window_start_date:surveillance_14_date, 
                .fns = ~ as_date(.x))) 

datatable(tab, filter = "top")

aux <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_14_date) |>
  mutate(across(.cols = dfs_date:surveillance_14_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_2_date:surveillance_14_date,
                .fns = ~ dfs_date < .x)) |>
  rowwise() |>
  mutate(n_biomarker_after_event = sum(c_across(surveillance_2_date:
                                                  surveillance_14_date), 
                                       na.rm = TRUE)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_14_date,
                .fns = ~ !is.na(.x))) |>
  mutate(total_biomarker = sum(c_across(surveillance_2_date:
                                          surveillance_14_date), 
                               na.rm = TRUE)) |>
  select(pts_id, n_biomarker_after_event, total_biomarker)

temp <- aux |> 
  select(-pts_id) |> 
  group_by(n_biomarker_after_event, total_biomarker) |>  # Direct grouping
  summarise(freq = n(), .groups = "drop")  # Drop groups after summarization


tab <- left_join(aux, dt) |>
  select(pts_id, n_biomarker_after_event, total_biomarker, 
         dfs_date,
         surveillance_2_date:surveillance_14_date) |>
  mutate(across(.cols = dfs_date:surveillance_14_date, 
                .fns = ~ as_date(.x))) |>
  filter(n_biomarker_after_event > 0)
datatable(tab, filter = "top")

aux <- tmerge(data1 = dt_survival, 
              data2 = dt_survival,
              id = pts_id, 
              dfs_event = event(dfs_time, dfs_event))
dt_final <- tmerge(data1 = aux, 
                   data2 = dt_biomarker,
                   id = pts_id, 
                   biomarker_status = 
                     tdc(biomarker_time, biomarker_status))

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
             data = dt_final)
summary(fit)
cox_fit_summary <- summary(fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#Time-dependent analysis for OS in longitudinal time points
```{r}
rm(list=ls())
setwd("~/Downloads")
dt <- read_xlsx("CLIA HNSCC Peddada Clinical Data_Time dependent.xlsx") |>
  clean_names() |>
  mutate(across(.cols = c(window_start_date,os_date,
                          surveillance_1_date:surveillance_13_date), 
                .fns = ~ as_date(as.Date(.x, format = "%Y-%m-%d"))))

dt_biomarker <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date,
         surveillance_1_status:surveillance_13_date) |>
  filter(ct_dna_surveillance_available) |>
  pivot_longer(cols = surveillance_1_status:surveillance_13_date,
               names_to = c("visit_number", ".value"),
               names_pattern = "surveillance_(.)_(.*)") |>
  mutate(biomarker_time = day(days(date - window_start_date))) |>
  select(pts_id, biomarker_time, biomarker_status = status) |>
  filter(!is.na(biomarker_time))

glimpse(dt_biomarker)

dt_survival <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date:os_date, os_event) |>  # Added os_event here
  filter(ct_dna_surveillance_available) |>
  mutate(dfs_time = (os_date - window_start_date),
         dfs_time = day(days(dfs_time)),
         os_event = as.numeric(os_event)) |>
  select(pts_id, dfs_time, os_event)

glimpse(dt_survival)

aux <- dt_survival %>% 
  filter(dfs_time <= 0)

tab <- left_join(aux, dt) |>
  select(pts_id, window_start_date, dfs_time, os_date,
         surveillance_1_date:surveillance_14_date) |>
  mutate(across(.cols = os_date:surveillance_14_date, 
                .fns = ~ as_date(.x))) |>
  select(pts_id, window_start_date, os_date, dfs_time)

datatable(tab, filter = "top")

dt_survival <- dt_survival |>
  filter(dfs_time > 0)

aux <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_14_date) |>
  mutate(across(.cols = surveillance_1_date:surveillance_14_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_14_date, 
                .fns = ~ .x < 0)) |>
  rowwise() |>
  mutate(sum_neg = 
           sum(c_across(surveillance_1_date:surveillance_14_date),
               na.rm = TRUE))  |>
  select(pts_id, sum_neg)

tab <- left_join(aux, dt) |>
  filter(sum_neg > 0) |>
  select(pts_id, sum_neg, window_start_date,
         surveillance_1_date:surveillance_14_date) |>
  mutate(across(.cols = window_start_date:surveillance_14_date, 
                .fns = ~ as_date(.x))) 

datatable(tab, filter = "top")

aux <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_14_date) |>
  mutate(across(.cols = dfs_date:surveillance_14_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_2_date:surveillance_14_date,
                .fns = ~ dfs_date < .x)) |>
  rowwise() |>
  mutate(n_biomarker_after_event = sum(c_across(surveillance_2_date:
                                                  surveillance_14_date), 
                                       na.rm = TRUE)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_14_date,
                .fns = ~ !is.na(.x))) |>
  mutate(total_biomarker = sum(c_across(surveillance_2_date:
                                          surveillance_14_date), 
                               na.rm = TRUE)) |>
  select(pts_id, n_biomarker_after_event, total_biomarker)

temp <- aux |> 
  select(-pts_id) |> 
  group_by(n_biomarker_after_event, total_biomarker) |>  # Direct grouping
  summarise(freq = n(), .groups = "drop")  # Drop groups after summarization


tab <- left_join(aux, dt) |>
  select(pts_id, n_biomarker_after_event, total_biomarker, 
         dfs_date,
         surveillance_2_date:surveillance_14_date) |>
  mutate(across(.cols = dfs_date:surveillance_14_date, 
                .fns = ~ as_date(.x))) |>
  filter(n_biomarker_after_event > 0)
datatable(tab, filter = "top")

aux <- tmerge(data1 = dt_survival, 
              data2 = dt_survival,
              id = pts_id, 
              os_event = event(dfs_time, os_event))
dt_final <- tmerge(data1 = aux, 
                   data2 = dt_biomarker,
                   id = pts_id, 
                   biomarker_status = 
                     tdc(biomarker_time, biomarker_status))

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, os_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, os_event) ~ biomarker_status,
             data = dt_final)
summary(fit)
cox_fit_summary <- summary(fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#PFS by ctDNA status at surveillance Stage I/II
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="I/II",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = TRUE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance Stage I/II", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxphf(surv_object ~ ctDNA.Surveillance, data=circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance Stage I/II", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at surveillance Stage III/IV
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="III/IV",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance Stage III/IV", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance Stage III/IV", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at surveillance p16(+)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Positive",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = TRUE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance p16(+)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxphf(surv_object ~ ctDNA.Surveillance, data=circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance p16(+)", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at surveillance p16(-)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Negative",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance p16(-)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance p16(-)", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#Multivariate cox regression for PFS ctDNA status at surveillance
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$cStage <- factor(circ_data$cStage, levels = c("I/II", "III/IV"))
circ_data$p16.status <- factor(circ_data$p16.status, levels = c("Negative", "Positive"))
circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Oropharynx", "Larynx/Hypopharynx", "Oral cavity", "Other (paranasal sinus and nasopharyngeal)"))
surv_object <- Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance + cStage + p16.status + Prim.Location, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for PFS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
```


#ctDNA and MTM/mL Dynamics for pts at surveillance window
```{r}
#Dynamics and MTM/mL plots for patients with ctDNA negative at surveillance
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC ctDNA MTM.csv", stringsAsFactors = FALSE)
df <- df[df$ctDNA.Surveillance=="NEGATIVE",]

df$PFS.Event <- ifelse(df$PFS.Event %in% c("No", "no", "FALSE", "False", "0"), FALSE,
                       ifelse(df$PFS.Event %in% c("Yes", "yes", "TRUE", "True", "1"), TRUE, NA))
df$PFS.Event <- factor(df$PFS.Event, levels = c(FALSE, TRUE))
df <- df %>%
  group_by(PatientName) %>%
  filter(n() >= 2) %>% #keep only pts with at least 2 post-surgery time points
  ungroup()

num_unique <- length(unique(df$PatientName))
cat("Number of unique patients:", num_unique, "\n")

df_patient_pfs <- df %>%
  group_by(PatientName) %>%
  dplyr::summarize(
    PFS_True = any(PFS.Event == TRUE, na.rm = TRUE),
    PFS_False = all(PFS.Event == FALSE, na.rm = TRUE)
  )

num_true <- sum(df_patient_pfs$PFS_True)
num_false <- sum(df_patient_pfs$PFS_False)

cat("Number of unique patients with Event:", num_true, "\n")
cat("Number of unique patients with No Event:", num_false, "\n")

p <- ggplot(df, aes(x = date.diff.months, 
                    y = MTM.mL, 
                    group = PatientName, 
                    color = PFS.Event)) +
  geom_line() +      # Connect timepoints for each patient
  geom_point() +     # Add points for each timepoint
  # Use a log10 scale for the y-axis with specified breaks
  scale_y_log10(breaks = c(0.01, 0.1, 1, 10, 100, 1000),
                labels = c("0.01","0.1", "1", "10", "100", "1000")) +
  scale_x_continuous(breaks = seq(0, max(df$date.diff.months, na.rm = TRUE), by = 6)) +
  scale_color_manual(values = c("FALSE" = "blue", "TRUE" = "red")) +
  labs(
    x = "Time Since Surgery or start of definitive treatment (months)",
    y = "Mean Tumor Molecules per mL (MTM/mL)",
    color = "PFS Event"
  ) +
  theme_minimal()
print(p)

#Dynamics and MTM/mL plots for patients with ctDNA positive at surveillance
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC ctDNA MTM.csv", stringsAsFactors = FALSE)
df <- df[df$ctDNA.Surveillance=="POSITIVE",]

df$PFS.Event <- ifelse(df$PFS.Event %in% c("No", "no", "FALSE", "False", "0"), FALSE,
                       ifelse(df$PFS.Event %in% c("Yes", "yes", "TRUE", "True", "1"), TRUE, NA))
df$PFS.Event <- factor(df$PFS.Event, levels = c(FALSE, TRUE))
df <- df %>%
  group_by(PatientName) %>%
  filter(n() >= 2) %>% #keep only pts with at least 2 post-surgery time points
  ungroup()

num_unique <- length(unique(df$PatientName))
cat("Number of unique patients:", num_unique, "\n")

df_patient_pfs <- df %>%
  group_by(PatientName) %>%
  dplyr::summarize(
    PFS_True = any(PFS.Event == TRUE, na.rm = TRUE),
    PFS_False = all(PFS.Event == FALSE, na.rm = TRUE)
  )

num_true <- sum(df_patient_pfs$PFS_True)
num_false <- sum(df_patient_pfs$PFS_False)

cat("Number of unique patients with Event:", num_true, "\n")
cat("Number of unique patients with No Event:", num_false, "\n")

p <- ggplot(df, aes(x = date.diff.months, 
                    y = MTM.mL, 
                    group = PatientName, 
                    color = PFS.Event)) +
  geom_line() +      # Connect timepoints for each patient
  geom_point() +     # Add points for each timepoint
  # Use a log10 scale for the y-axis with specified breaks
  scale_y_log10(breaks = c(0.01, 0.1, 1, 10, 100, 1000, 10000),
                labels = c("0.01","0.1", "1", "10", "100", "1000", "10000")) +
  scale_x_continuous(breaks = seq(0, max(df$date.diff.months, na.rm = TRUE), by = 6)) +
  scale_color_manual(values = c("FALSE" = "blue", "TRUE" = "red")) +
  labs(
    x = "Time Since Surgery or start of definitive treatment (months)",
    y = "Mean Tumor Molecules per mL (MTM/mL)",
    color = "PFS Event"
  ) +
  theme_minimal()
print(p)
```

#PFS by ctDNA status at surveillance for pts with MRD & Surveillance time points available
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.complete=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#Time-dependent analysis for PFS in longitudinal time points for pts with MRD & Surveillance time points available
```{r}
rm(list=ls())
setwd("~/Downloads")
dt <- read_xlsx("CLIA HNSCC Peddada Clinical Data_Time dependent.xlsx") |>
  clean_names() |>
  mutate(across(.cols = c(window_start_date,dfs_date,
                          surveillance_1_date:surveillance_14_date), 
                .fns = ~ as_date(as.Date(.x, format = "%Y-%m-%d"))))

dt_biomarker <- dt |>
  select(pts_id, ct_dna_complete,
         window_start_date,
         surveillance_1_status:surveillance_14_date) |>
  filter(ct_dna_complete) |>
  pivot_longer(cols = surveillance_1_status:surveillance_14_date,
               names_to = c("visit_number", ".value"),
               names_pattern = "surveillance_(.)_(.*)") |>
  mutate(biomarker_time = day(days(date - window_start_date))) |>
  select(pts_id, biomarker_time, biomarker_status = status) |>
  filter(!is.na(biomarker_time))

glimpse(dt_biomarker)

dt_survival <- dt |>
  select(pts_id, ct_dna_complete,
         window_start_date:dfs_date, dfs_event) |>  # Added dfs_event here
  filter(ct_dna_complete) |>
  mutate(dfs_time = (dfs_date - window_start_date),
         dfs_time = day(days(dfs_time)),
         dfs_event = as.numeric(dfs_event)) |>
  select(pts_id, dfs_time, dfs_event)

glimpse(dt_survival)

aux <- dt_survival %>% 
  filter(dfs_time <= 0)

tab <- left_join(aux, dt) |>
  select(pts_id, window_start_date, dfs_time, dfs_date,
         surveillance_1_date:surveillance_14_date) |>
  mutate(across(.cols = dfs_date:surveillance_14_date, 
                .fns = ~ as_date(.x))) |>
  select(pts_id, window_start_date, dfs_date, dfs_time)

datatable(tab, filter = "top")

dt_survival <- dt_survival |>
  filter(dfs_time > 0)

aux <- dt |>
  select(pts_id, ct_dna_complete,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_14_date) |>
  mutate(across(.cols = surveillance_1_date:surveillance_14_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_14_date, 
                .fns = ~ .x < 0)) |>
  rowwise() |>
  mutate(sum_neg = 
           sum(c_across(surveillance_1_date:surveillance_14_date),
               na.rm = TRUE))  |>
  select(pts_id, sum_neg)

tab <- left_join(aux, dt) |>
  filter(sum_neg > 0) |>
  select(pts_id, sum_neg, window_start_date,
         surveillance_1_date:surveillance_14_date) |>
  mutate(across(.cols = window_start_date:surveillance_14_date, 
                .fns = ~ as_date(.x))) 

datatable(tab, filter = "top")

aux <- dt |>
  select(pts_id, ct_dna_complete,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_14_date) |>
  mutate(across(.cols = dfs_date:surveillance_14_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_2_date:surveillance_14_date,
                .fns = ~ dfs_date < .x)) |>
  rowwise() |>
  mutate(n_biomarker_after_event = sum(c_across(surveillance_2_date:
                                                  surveillance_14_date), 
                                       na.rm = TRUE)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_14_date,
                .fns = ~ !is.na(.x))) |>
  mutate(total_biomarker = sum(c_across(surveillance_2_date:
                                          surveillance_14_date), 
                               na.rm = TRUE)) |>
  select(pts_id, n_biomarker_after_event, total_biomarker)

temp <- aux |> 
  select(-pts_id) |> 
  group_by(n_biomarker_after_event, total_biomarker) |>  # Direct grouping
  summarise(freq = n(), .groups = "drop")  # Drop groups after summarization


tab <- left_join(aux, dt) |>
  select(pts_id, n_biomarker_after_event, total_biomarker, 
         dfs_date,
         surveillance_2_date:surveillance_14_date) |>
  mutate(across(.cols = dfs_date:surveillance_14_date, 
                .fns = ~ as_date(.x))) |>
  filter(n_biomarker_after_event > 0)
datatable(tab, filter = "top")

aux <- tmerge(data1 = dt_survival, 
              data2 = dt_survival,
              id = pts_id, 
              dfs_event = event(dfs_time, dfs_event))
dt_final <- tmerge(data1 = aux, 
                   data2 = dt_biomarker,
                   id = pts_id, 
                   biomarker_status = 
                     tdc(biomarker_time, biomarker_status))

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
             data = dt_final)
summary(fit)
cox_fit_summary <- summary(fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#Median numbers of time points and lead time in the longitudinal setting for pts with MRD & Surveillance time points available
```{r}
# Load the dataset
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.complete=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

median_Nsurvtps <- median(circ_datadf$Nsurvtps, na.rm = TRUE)
min_Nsurvtps <- min(circ_datadf$Nsurvtps, na.rm = TRUE)
max_Nsurvtps <- max(circ_datadf$Nsurvtps, na.rm = TRUE)

cat(sprintf("Median # of surveillance time points: %d (%d-%d)\n", 
            median_Nsurvtps, min_Nsurvtps, max_Nsurvtps))
```

#Median of median interval of ctDNA timepoints and radiological imaging assessment
```{r}
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC UNM_OP.csv", stringsAsFactors = FALSE)

names(df) <- trimws(names(df))

# ---- Helper: compute median interval per patient ----
median_interval_per_patient <- function(data, filter_col, filter_val) {
  data %>%
    filter(!!sym(filter_col) == filter_val) %>%
    arrange(PatientName, date.diff) %>%
    group_by(PatientName) %>%
    mutate(interval = date.diff - lag(date.diff)) %>%
    summarise(median_interval = median(interval, na.rm = TRUE), .groups = "drop") %>%
    mutate(event_group = filter_val)
}

# ---- Compute per-patient medians ----
ctDNA_patient_medians   <- median_interval_per_patient(df, "Event", "ctDNA")
imaging_patient_medians <- median_interval_per_patient(df, "Event_type", "Imaging")

# ---- Function to summarize patient-level medians to cohort-level ----
cohort_summary <- function(patient_data, event_label) {
  # Filter out patients with NA median intervals
  valid_data <- patient_data %>% filter(!is.na(median_interval))
  
  data.frame(
    Event_Type = event_label,
    Cohort_Median_Frequency = median(valid_data$median_interval, na.rm = TRUE),
    Min_Patient_Median = min(valid_data$median_interval, na.rm = TRUE),
    Max_Patient_Median = max(valid_data$median_interval, na.rm = TRUE),
    Range_Patient_Median = max(valid_data$median_interval, na.rm = TRUE) - 
      min(valid_data$median_interval, na.rm = TRUE)
  )
}

# ---- Combine all results ----
results <- bind_rows(
  cohort_summary(ctDNA_patient_medians, "ctDNA"),
  cohort_summary(imaging_patient_medians, "Imaging")
)

# ---- Print cohort-level summary ----
cat("===== Median Frequency (Days) per Cohort =====\n")
print(results, row.names = FALSE)

# ---- OPTIONAL: Save patient-level medians ----
patient_level_medians <- bind_rows(ctDNA_patient_medians, imaging_patient_medians)
#write.csv(patient_level_medians, "patient_median_intervals.csv", row.names = FALSE)
```

#Plot for individual lead-time calculations for each pt
```{r}
rm(list=ls())
csv_path <- "~/Downloads/CLIA HNSCC Peddada_Lead Time pts.csv"
df <- read.csv(csv_path, check.names = FALSE)
id_candidates <- c("Patient","ID","Pt","Subject","Sample")
id_col <- id_candidates[id_candidates %in% names(df)][1]
if (is.na(id_col)) {
  df <- df %>% mutate(Patient = row_number())
  id_col <- "Patient"
}

num_cols <- intersect(c("MR","CR","LT"), names(df))
df[num_cols] <- lapply(df[num_cols], function(x) suppressWarnings(as.numeric(x)))

# If LT missing, compute in days
if (!("LT" %in% names(df))) {
  df <- df %>% mutate(LT = CR - MR)
}

# ---- convert to months ----
# Approximate: 30.44 days per month (average)
days_to_months <- function(x) x / 30.437

df <- df %>%
  mutate(MR_mo = days_to_months(MR),
         CR_mo = days_to_months(CR),
         LT_mo = days_to_months(LT))

# ---- ordering for y-axis ----
df <- df %>%
  mutate(Earliest = pmin(MR_mo, CR_mo, na.rm = TRUE)) %>%
  arrange(Earliest) %>%
  mutate(Patient_f = factor(.data[[id_col]], levels = .data[[id_col]]))

# ---- long format for points ----
points_long <- df %>%
  select(Patient_f, MR_mo, CR_mo) %>%
  pivot_longer(c(MR_mo, CR_mo), names_to = "Type", values_to = "Months") %>%
  mutate(Type = dplyr::recode(Type,
                              "MR_mo" = "Molecular recurrence",
                              "CR_mo" = "Clinical recurrence"))

# ---- segments between MR and CR ----
segments_df <- df %>%
  transmute(Patient_f,
            x0 = MR_mo, x1 = CR_mo,
            lt_flag = if_else(LT > 120, "gt120", "le120"))

# ---- annotation ----
med_lt  <- median(df$LT_mo, na.rm = TRUE)
min_lt  <- min(df$LT_mo, na.rm = TRUE)
max_lt  <- max(df$LT_mo, na.rm = TRUE)

annot_label <- sprintf("Median lead-time:\n%.1f months (%.1f to %.1f)",
                       med_lt, min_lt, max_lt)

# ---- plot ----
pal <- c("Molecular recurrence" = "#10B4C1",
         "Clinical recurrence"  = "#C96A72")

x_max <- max(c(df$MR_mo, df$CR_mo), na.rm = TRUE)
y_mid <- levels(df$Patient_f)[ceiling(nlevels(df$Patient_f) * 0.55)]

p <- ggplot() +
  geom_segment(data = segments_df,
               aes(x = x0, xend = x1, y = Patient_f, yend = Patient_f,
                   linetype = lt_flag),
               linewidth = 0.6, color = "black") +
  scale_linetype_manual(values = c(le120 = "solid", gt120 = "dashed"),
                        guide = "none") +
  geom_point(data = points_long,
             aes(x = Months, y = Patient_f, color = Type),
             size = 2.8) +
  scale_color_manual(values = pal, name = NULL) +
  labs(x = "Months from Surgery or Definitive Treatment", y = NULL) +
  annotate("text",
           x = x_max * 0.73,
           y = y_mid,
           label = annot_label,
           hjust = 0, vjust = 0.5, size = 3.8) +
  scale_x_continuous(breaks = seq(0, ceiling(x_max/3)*3, by = 3)) +
  coord_cartesian(xlim = c(0, x_max * 1.05)) +
  theme_bw(base_size = 12) +
  theme(
    panel.grid.major = element_line(color = "grey85", linewidth = 0.3),
    panel.grid.minor = element_line(color = "grey92", linewidth = 0.2),
    axis.text.y = element_text(size = 9)
  )

print(p)

wc_df <- df %>% dplyr::select(MR, CR) %>% tidyr::drop_na()
paired_diff <- wc_df$CR - wc_df$MR

wilx <- wilcox.test(
  x = wc_df$CR, y = wc_df$MR,
  paired = TRUE,
  alternative = "two.sided",
  conf.int = TRUE,      # gives CI for the Hodges–Lehmann estimate of the median difference
  exact = FALSE         # safer for ties/large N
)

W_stat   <- unname(wilx$statistic)          # Wilcoxon V
p_value  <- wilx$p.value
HL_est   <- unname(wilx$estimate)           # median of (CR - MR)
HL_ci    <- wilx$conf.int                   # CI for median difference

# Simple p-value formatter for annotation/publication
fmt_p <- function(p) {
  if (is.na(p)) return("NA")
  if (p < 0.001) "< 0.001" else sprintf("= %.3f", round(p, 3))
}
p_text <- paste0("P ", fmt_p(p_value))  # e.g., "P = 0.003" or "P < 0.001"

# Optional: print a compact summary
cat("\nPaired Wilcoxon signed-rank test (CR vs MR)\n",
    "-------------------------------------------\n",
    sprintf("N pairs: %d", nrow(wc_df)), "\n",
    sprintf("W (V): %g", W_stat), "\n",
    sprintf("P-value: %s", ifelse(p_value < 0.001, "< 0.001", sprintf("%.6f", p_value))), "\n",
    sprintf("Hodges–Lehmann median difference (CR - MR): %.1f days", HL_est), "\n",
    sprintf("95%% CI: [%.1f, %.1f] days", HL_ci[1], HL_ci[2]), "\n", sep = "")
```


#ctDNA velocity and lead time liner regression
```{r}
csv_path <- "~/Downloads/CLIA HNSCC Peddada_ctDNA velocity.csv"  # <- adjust if needed
df_raw <- read.csv(csv_path, check.names = FALSE)
df <- df_raw %>%
  rename(MTM_mL = `MTM/mL`) %>%
  mutate(
    daysCR.months = as.numeric(daysCR.months),
    MTM_mL = as.numeric(MTM_mL)
  ) %>%
  filter(!is.na(PatientName), !is.na(daysCR.months), !is.na(MTM_mL))

preCR <- df %>%
  filter(daysCR.months <= 0, is.finite(MTM_mL), MTM_mL > 0)

eligible <- preCR %>%
  group_by(PatientName) %>%
  filter(n() >= 2, n_distinct(daysCR.months) >= 2) %>%
  ungroup()
if (nrow(eligible) == 0) {
  warning("No patients have ≥2 valid pre-recurrence points with distinct times; regression lines will be omitted.")
}

fits <- eligible %>%
  group_by(PatientName) %>%
  summarise(x_min = min(daysCR.months, na.rm = TRUE), .groups = "drop") %>%
  mutate(grid = map(x_min, ~seq(.x, 0, length.out = 50))) %>%
  select(PatientName, grid)

predict_patient <- function(dat, newx) {
  if (length(newx) == 0) {
    return(tibble(daysCR.months = numeric(0), MTM_mL = numeric(0)))
  }
  dat2 <- dat %>%
    filter(is.finite(MTM_mL), MTM_mL > 0) %>%
    mutate(log_ctdna = log10(MTM_mL))
  if (nrow(dat2) < 2 || n_distinct(dat2$daysCR.months) < 2 || any(!is.finite(dat2$log_ctdna))) {
    return(tibble(daysCR.months = numeric(0), MTM_mL = numeric(0)))
  }
  m <- lm(log_ctdna ~ daysCR.months, data = dat2)
  tibble(
    daysCR.months = newx,
    MTM_mL = 10 ^ predict(m, newdata = tibble(daysCR.months = newx))
  )
}

pred_lines <- eligible %>%
  group_by(PatientName) %>%
  tidyr::nest() %>%                       # list-column "data" per patient
  left_join(fits, by = "PatientName") %>% # list-column "grid" per patient
  mutate(pred = map2(data, grid, ~predict_patient(.x, .y))) %>%
  select(PatientName, pred) %>%
  tidyr::unnest(pred)

pooled_line <- {
  if (nrow(preCR) >= 2 && n_distinct(preCR$daysCR.months) >= 2) {
    pooled_x <- seq(min(preCR$daysCR.months, na.rm = TRUE), 0, length.out = 100)
    pooled_fit <- lm(log10(MTM_mL) ~ daysCR.months, data = preCR)
    tibble(
      daysCR.months = pooled_x,
      MTM_mL = 10 ^ predict(pooled_fit, newdata = tibble(daysCR.months = pooled_x))
    )
  } else {
    tibble(daysCR.months = numeric(0), MTM_mL = numeric(0))
  }
}

x_min <- floor(min(df$daysCR.months, na.rm = TRUE) / 3) * 3
x_max <- ceiling(max(df$daysCR.months, na.rm = TRUE) / 3) * 3
y_min_pos <- max(min(df$MTM_mL[df$MTM_mL > 0], na.rm = TRUE) / 2, 0.01)
y_max_pos <- 10 ^ ceiling(log10(max(df$MTM_mL, na.rm = TRUE)))
log_breaks <- 10 ^ seq(floor(log10(y_min_pos)), ceiling(log10(y_max_pos)))

p <- ggplot() +
  # per-patient fitted lines (if any)
  geom_line(data = pred_lines,
            aes(x = daysCR.months, y = MTM_mL, color = PatientName),
            linewidth = 1) +
  # pooled dashed trend (if any)
  geom_line(data = pooled_line,
            aes(x = daysCR.months, y = MTM_mL),
            linewidth = 0.8, linetype = "dashed", color = "grey35") +
  # raw points (all timepoints, before and after)
  geom_point(data = df,
             aes(x = daysCR.months, y = MTM_mL, color = PatientName),
             size = 1.8, alpha = 0.85) +
  # vertical line at imaging-positive date
  geom_vline(xintercept = 0, linetype = "dashed", linewidth = 0.8, color = "black") +
  scale_y_log10(breaks = log_breaks,
                labels = label_number(accuracy = 1)) +
  scale_x_continuous(breaks = seq(x_min, x_max, by = 3)) +  # every 3 months
  labs(
    x = "Months before/after clinical recurrence (0 = imaging positive)",
    y = "ctDNA level (MTM/mL)",
    color = "Patient"
  ) +
  theme_bw(base_size = 12) +
  theme(
    panel.grid.major = element_line(color = "grey85", linewidth = 0.3),
    panel.grid.minor = element_line(color = "grey92", linewidth = 0.2),
    legend.position = "none"  # change to "right" if you want the legend
  )

print(p)
```


#Patient Specific Plot for UNM-069
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC_Pt Specific Plot data.csv")

library(ggplot2)
library(dplyr)
library(scales)

# 1. FILTER FOR PATIENT
target_patient <- "UNM-069"
plot_data <- circ_data %>% filter(PatientName == target_patient)

# 2. Process Data
# We fix the pseudo-zero for log scale but allow high values (like 18.08) to remain
data_Signatera <- plot_data %>% 
  filter(Event_type %in% c("ctDNA_pos", "ctDNA_neg")) %>%
  mutate(MTM_Log = ifelse(MTM.mL == 0, 0.001, MTM.mL))

data_Imaging_NED <- plot_data %>% filter(Event_type == "Imaging" & Event == "NED")
data_Imaging_PD <- plot_data %>% filter(Event_type == "Imaging" & Event == "PD")
data_Biopsy <- plot_data %>% filter(Event_type == "Biopsy" | Event == "Biopsy")

data_Treatment <- plot_data %>% 
  filter(!is.na(Tx_type) & Tx_type != "NA" & Tx_type != "" & !is.na(Tx_start.months)) %>%
  distinct(Tx_type, Tx_start.months, Tx_end.months)

# 3. Create the Plot
ggplot() +
  # Treatment Rectangles - Expanded ymax to match new scale
  geom_rect(data = data_Treatment, 
            aes(xmin = Tx_start.months, xmax = Tx_end.months, ymin = 0.004, ymax = 100), 
            fill = "purple", alpha = 0.15) +
  geom_text(data = data_Treatment, 
            aes(x = (Tx_start.months + Tx_end.months)/2, y = 50, label = Tx_type), 
            angle = 90, color = "purple", size = 3, fontface = "bold") +
  
  # ctDNA Line - Now it will connect because the limits are higher
  geom_line(data = data_Signatera, aes(x = date.diff.months, y = MTM_Log), 
            color = "black", linewidth = 0.7) +
  
  # ctDNA Points
  geom_point(data = data_Signatera, 
             aes(x = date.diff.months, y = MTM_Log, fill = Event_type), 
             shape = 21, size = 3.5, color = "black") +
  
  # Clinical Events
  geom_point(data = data_Imaging_NED, aes(x = date.diff.months, y = 0.005, color = "NED in Scan"), 
             shape = 25, size = 4, fill = "green") +
  geom_point(data = data_Imaging_PD, aes(x = date.diff.months, y = 0.005, color = "PD (Progression)"), 
             shape = 25, size = 4, fill = "red") +
  geom_point(data = data_Biopsy, aes(x = date.diff.months, y = 0.005, color = "Biopsy"), 
             shape = 23, size = 4, fill = "orange") +
  
  # X-Axis: 3-month intervals
  scale_x_continuous(breaks = seq(0, max(plot_data$date.diff.months, na.rm=T) + 6, by = 3)) +
  
  # Y-Axis: EXPANDED Log10 scale to include 18.08
  scale_y_log10(breaks = c(0.001, 0.01, 0.1, 1, 10, 100),
                labels = c("0", "0.01", "0.1", "1", "10", "100"),
                limits = c(0.001, 100)) +
  
  scale_fill_manual(values = c("ctDNA_pos" = "black", "ctDNA_neg" = "white"), 
                    labels = c("Negative", "Positive"), name = "ctDNA Status") +
  
  scale_color_manual(values = c("NED in Scan" = "darkgreen", 
                                "PD (Progression)" = "darkred", 
                                "Biopsy" = "darkorange"), 
                     name = "Clinical Events") +
  
  guides(color = guide_legend(override.aes = list(shape = c(23, 25, 25), 
                                                  fill = c("orange", "green", "red")))) +
  
  labs(x = "Months from Diagnosis/Surgery", 
       y = "MTM/mL", 
       title = paste("Clinical Timeline:", target_patient)) +
  theme_minimal() +
  theme(panel.grid.minor = element_blank())
```

